Data Mining in Social Networks Linkedin

Data Mining in Social Networks:

Linkedin.com

Background

Social networks have gained mass popularity in

recent years. While social networks help share

information and connect like-minded people,

they can also lead to misuse of or unintended use

of data that they share through data mining

attempts. One of the most commonly used

professional social networks is linkedin.com.

Linkedin.com was launched in 2003 and as of

October 2009, has 50 million users worldwide

with 11 million users in Europe alone (Weiner,

2009). India is the fastest-growing country with

3 million users and rising day by day (Weiner,

2009). As Jeff Weiner (2009) says, LinkedIn’s

mission is “to connect the world’s professionals

to make them more productive and successful”.

How far LinkedIn has been successful in this

attempt is interesting research by itself but more

importantly, I find it more interesting to explore

whether data mining is assisting LinkedIn in

achieving its goal of creating further

roadblocks? Getting a user to join LinkedIn is

one thing, but being able to retain that user is

another thing. So have users found LinkedIn as

useful as it claims and have they continued to

use LinkedIn or have they moved on? Survival

data mining techniques can help answer this

question. The survival data mining technique is

an interesting technique that provides “rapid

feedback about the customers and their

behaviors, while at the same time providing a

solid basis for quantifying customer value and

measuring customer loyalty” (Linoff, 2004).

Aims

The project aims to understand LinkedIn users,

improve user retention and a user lifetime to

improve profit margins using survival data

mining techniques:

1. to understand value addition such data

mining can result into.

2. to understand users, their behaviors, and

preferences.

3. to understand what strategies work best for

4. to understand how LinkedIn data can be

misused, used against the user, or made

unintentional use of without permission

and how that can undermine the LinkedIn

goals.

5. to understand what steps can be taken and

what needs to be taken to reduce the

chances of data being misused thereby

improving chances of user retention.

Methodology

One of the ways to achieve the aim is to

understand how users view data mining of their

data on LinkedIn. This can be achieved by

interviews or surveys of LinkedIn users whose

expectations and concerns regarding the usage

of their data can be noted down and their

experience regarding the same can be found out.

The second methodology to be employed is to

harvest data directly from LinkedIn. This will

serve as a proof of concept as to how easy or

difficult it is to conduct data mining from

LinkedIn to automatically gather large amounts

of data and draw conclusions on usage trends.

Since there is a multitude of questions to be

answered through this research, a variety of

techniques will be used including cluster

analysis, classification and prediction, and

statistical analysis. This data will also be used to

perform survival data mining to understand how

users can be retained.

*Research Questions

The research seeks to find the answers to the

following questions:

1. How can survival data mining provide

insights into users?

2. What are the different types of LinkedIn

users?

3. How can survival data mining be used to

improve user retention?

4. How can data mining be used on LinkedIn

to provide more value-added features to its

users?

5. How can data mining reveal usage trends

useful for conducting market research?

6. Can data mining be used on LinkedIn to

help identify crime associations, terrorist

activities as can be seen as possibilities

with another social network?

7. How can data mining lead to misuse of

information provided or unintended use of

information that takes place without the

user’s permission?

8. What steps can a LinkedIn user take to

protect his privacy? What are the tradeoffs

in that case?

*Review of the literature

LinkedIn as a business-oriented social

networking site has caught the attention of many

researchers (e.g. O’Murchu et al, 2004;

Churchill et al, 2005); however, it is used more

as an example or for comparison with other

social networking sites. There has been little

research on the usefulness, privacy, and other

data mining aspects although there are blogs and

news articles on LinkedIn that are interesting. In

a related field, there has been similar research

conducted on social networking sites aimed at

having more friendly than professional contacts

including FaceBook and MySpace (e.g. Jones et

al, 2005). Data mining on social networks, in

general, has also been researched well enough

with security and privacy on social networks

receiving the most attention. For example,

Clifton et al (1996) discuss the security and

privacy implications on social networking and

provide several methods that could be used to

prevent data mining such as fuzzing the data,

eliminating unnecessary groupings, audits,

augmenting the data, etc. Seifert (2007) and Pant

et al (2009) discuss data mining on social

networks as a means to detect fraud and terrorist

activities.

Survival data mining approaches have been

researched in the medical fields and other areas

but not applied to social networks like LinkedIn.

Linoff (2004) explains how survival data mining

can be applied to a subscription-based business

model.

Expected Outcomes, Significance, or

Rationale

LinkedIn is growing as a professional

networking site and the data stored there could

have lots of potential for positive research both

for the benefit of the user as well as for the

business. However, understanding how it can be

misused is more important since it could cause

harm to the professional image of a user of this

site. The research will look at ways that data

mining could benefit the users of this site and the

business itself positively. The research will also

look at how data mining could prove detrimental

Phase

Start

Finish

Literature Review

Ongoing

15thJuly 2010

Chapter Writing – Draft

1stJuly 2010

16thSept 2009

Data Collection – Survey

16thSept 2010

10thJan 2011

Chapter Writing – Draft

11thJan 2011

15thFeb 2011

Data Collection – Data Harvesting

20thFeb 2011

15thDec 2011

Chapter Writing – Draft

16thNov 2011

15thJan 2012

Survival Data Mining Analysis

15thJan 2012

15thJun 2012

Chapter Writing – Draft

15thJun 2012

15thSept 2012

Reporting

16thSept 2012

1stNov 2012

Chapter Writing – Draft

1stNov 2012

1stJan 2013

to the users and what they can do to prevent such

incidents from happening.

*Timetable

Finalization and Editing

1stJan 2013

1stMar 2013

Thesis Submission Date

1stMar 2013

List of References

ATA, N., ÖZKÖK, E. & KARABEY, U. (2005)

“SURVIVAL DATA MINING: AN

APPLICATION TO CREDIT CARD

HOLDERS”. Journal of Engineering and

Natural Sciences, 26(1), 33-42.

Bishop, K., Draskovich, J., Hottenroth, A., Lee,

B. & Pesavento, S. (2005) Business Uses of Data

Mining and Data Warehousing. Web.

Breiger, R. L., Carley, K. M. & Pattison, P.

(2003) “Dynamic Social Network Modeling and

Analysis: workshop summary and

papers”. National Academics Press.

Churchill, E. F. & Halverson, C. A. (2005)

“Social Networks and Social

Networking”. IEEE Computer Society, 2005,

14-19

Clifton, C. & Marks, D. (1996) “Security and

Privacy Implications of Data

Mining”. Proceedings of the 1996 SIGMOD

Workshop on Data Mining and Knowledge

Discovery.

Collica, R. (2004) “Data Mining Galore: For

Business Applications”. Web.

Han, J. & Kamber, M. (2006) “Data mining:

concepts and techniques “. Elsevier Inc..

Han, J. & Kamber, M. (2006) “Data mining:

concepts and techniques “. Morgan Kaufmann.

Jensen, D. & Neville, J. (n.d.) Data Mining in

Social Networks. Web.

Jones, H. & Soltren, J. H. (2005) Facebook:

Threats to Privacy. Web.

Kleinberg, J. M. (2007) “Challenges in mining

social network data: processes, privacy, and

paradoxes”. Proceedings of the 13th ACM

SIGKDD international conference on

Knowledge discovery and data mining.

KUSIAK, A., DIXON, B. & SHAH, S. (2006)

“Predicting survival time for kidney dialysis

patients: a data mining approach”. Computers in

Biology and Medicine, 35(4), 311-327.

Linoff, G. S. (2004) “Survival Data Mining”.

Web.

Linoff, G. S. (2004) Survival Data Mining for

Customer Insight. Web.

Mohammed, Z & Kotze, D (2005) “Survival

data mining in the telecommunications

industries: usefulness and complications “. Data

Mining XI: Data Mining, Text Mining and Their

Business Applications., 505-512

Olson, D. L. & Delen, D. (2008) “Advanced

Data Mining Techniques”. Springer.

O’Murchu, I., Breslin, J. G., and Decker, S.

(2004): “Online Social and Business

Networking Communities”. Technical Report.

Pant, D. & Sharma, M. K. (2009) “Web Mining

and Social Network Analysis in Cyber war, to

warn about terrorist attacks”. Web.

Potts, W. (2006) Survival Data Mining. Web.

Seifert, J. W. (2009) Data Mining and Homeland

Security: An Overview . Web.

Wang, J. (2009) “Encyclopedia of Data

Warehousing and Mining “. Information Science

Reference.

Weiner, J. (200) LinkedIn: 50 million

professionals worldwide. Web.

Data Mining Classifiers: The Advantages and

Disadvantages

Decision Trees: C4.5 Classifier

Advantages

Classifiers produced by C4.5 are either

expressed as decision trees or rulesets. Our focus

in this discussion is on decision trees, and we

look at their advantages. The C4.5 classifier is

based on the ID32 algorithm, whose primary

aim is to find small decision trees. Based on this,

we can say that the decision trees produced are

small and simple to understand (Witten, Frank,

2000).

Another advantage of this classifier is that the

source code is readily available. Thirdly,

unavailable attribute values are accounted for in

C4.5 by assessing the gain using the records

where the particular attribute is defined. A fourth

advantage is that; it can handle both continuous

and discrete attributes. Attributes with a

continuous range, we create partitions based on

a predetermined pattern in the training set and

calculate the gain on each partition, (Quinlan,

1993). Then the partition that maximizes the

gain is picked. Lastly, tree pruning after creation

ensures tree simplicity by replacing some

branches with leaf nodes.

Disadvantages

C4.5 classifiers are basically slower in terms of

processing speed. For instance, a task that will

take C4.5 15hours to complete; C5.0 will take

only 2.5 minutes. Secondly, it is inefficient in

memory usage meaning that some tasks will not

complete on 32-bit systems (Witten, Frank,

2000). In terms of accuracy, the rule sets

produced by C4.5 classifiers basically contain

many errors. C4.5 has limited data types and

lacks the facility to label data as not applicable,

(Quinlan, 1993). The decision trees produced by

C4.5 are relatively bigger but these are catered

for in the C5.0 version. All classification errors

are treated equally but some are more serious

than others and this is another disadvantage.

Lack of a provision to quantify the importance

of cases is a disadvantage because not all cases

are equally important. Lastly, attributes need to

be winnowed before a classifier is generated and

C4.5 lacks this facility.

KStar Algorithm classifiers

Advantages

Firstly, In terms of accuracy, this algorithm is

comparable to C4.5 on voluminous UCI

benchmark datasets. KStar performs better and

with a higher speed than C4.5 on big numbers of

text classifications. Thirdly, this algorithm is

low in time complexity that means it is very fast.

Its speed can be compared to that of naive

Bayes. In addition, this algorithm can be

speeded up by combining it with other scaling-

up methods (Cormen et al 1990). Another

advantage is that it uses entropy as a measure of

distance hence providing a consistent approach

to management of symbolic attributes, unknown

values and continuous value attributes. The

results presented compare satisfactorily with

many machine learning algorithms. This

algorithm is an instance-based classifier. It

classifies an instance by comparison; an instance

is compared to pre-classified examples stored in

a database. New instances to be added to the

instance database and choice of instances from

the database to be used in classification are

determined by a concept description updater

(Gray, 1990). This helps reduce memory space

requirements and also improve tolerance to

noise in data.

Disadvantages

One of the major disadvantages of this algorithm

is the fact that it has to generate distance

measures for all the recorded attributes (Cormen

et al 1990). Another disadvantage is that it is not

cost effective. Both building and learning

processes are quite expensive. In terms of

memory space, as much as the algorithm uses an

instance updater to select and determine the

instances to be used, the storage of sample

instances is also involving in terms of memory

space (Gray, 1990). Although the algorithm can

be speeded up by combining it with other

algorithms, it is sometimes a disadvantage since

the combination process involves extra costs.

Bayesian Network classifier

Advantages

The first advantage of this classifier is its

computational efficiency. The representation of

large and complex computational problems is

decomposed into smaller and simpler self

sufficient models to enhance efficiency.

Secondly, it simplifies the incorporation of

domain knowledge into the model design by

using the structure of problem domain. Thirdly,

the natural combination of EM algorithm and the

probabilistic representation helps address the

problems with missing data. The ability of this

classifier to indicate all the possible classes a

new sample may belong and the probability, is

another advantage since in case of a

misclassification, it is easy to tell which other

class the sample may belong, (Mitchell,

1997).Another advantage is the presence of an

updater that enables the classifier to learn from

new data samples. Bayesian networks have the

advantage of being able to capture the

complexity of decision making. Lastly, the

system’s rules of classification can be

semantically determined and justified to both the

novice and the expert (Jensen, Graven 2007).

Disadvantages

The mismatch between the data likelihood and

the actual label prediction accuracy tends to

make the learning method suboptimal. Secondly,

Bayesian networks require an expert to give

domain information for the creation of the

network (Jensen, Graven 2007). Despite the

substantial amount of research carried out, the

creation of these networks is still limited to data

sets consisting of only a few variables which are

very informative. Another disadvantage of these

networks is the fact that their interpretation and

efficiency is quite limited when rulesets are

drawn from the network. In comparison to rules

derived from decision trees whose interpretation

is simple and direct, Bayesian networks are

more complex (Mitchell, 1997).

Advantages

The first advantage of this classifier is the use of

propositional rule learner which ensures that

errors are minimal. Also, the phase by phase

implementation of the algorithm ensures that the

overall results are near perfect. During the rule

set growing phase, the condition with the highest

information gain is picked and this ensures that

the rule set is perfect (Arthur, 1996). Another

advantage is that the algorithm is made shorter

and simpler by pruning any useless parts of a

rule. This makes the classifier easy to

understand and interpret. The ease of generation

of this classifier is a fourth advantage. This

classifier is highly expressive; it is comparable

to a decision tree. Even in terms of performance,

it can be ranked the same level as a decision tree

[Bishop, 1997]. JRip classifier has the ability to

classify new instances rapidly. Lastly, it is easy

for this classifier to handle missing values as

well as numeric attributes.

Disadvantages

JRip algorithm requires a large investment in

terms of time to learn the algorithm and test the

features that can be customized. The fact that the

RIPPER algorithm uses some induced rules,

which sometimes have to be replaced by expert-

derived rules for some applications is another

disadvantage. In addition, the accuracy of JRip’s

results sometimes vary accordingly. This is

because; the results produced differ depending

on the option of the rule voting method used

(Authur, 1996). Another disadvantage is that,

highly accurate results can only be achieved by

running the algorithm many times. Assignment

of salience which is an order of that has been

prescribed for firing rules may lend the expert

system’s inference engine powerless. This may

also have negative effects on the performance of

such a system which is rule-based (Bishop,

1997).

K-Nearest Neighbour Algorithm

Advantages

This algorithm uses local information. This kind

of information can produce highly adaptive

behaviour. Secondly this algorithm is robust to

noisy training data and it is simple to implement.

It is also very easy to use in parallel

implementations. Another advantage of K-

Nearest neighbour algorithm is the ease and

simplicity of learning. In addition the training

speed is very high and the results are nearly

optimal in the case of a large sample limit. This

means that the algorithm is more effective if the

set of training data is large [Witten, 2005] The

ability of this algorithm to approximate complex

concepts of the target locally and also differently

for every new instance is another advantage of

this algorithm. Lastly, the algorithm is intuitive

and easy to understand. This makes

implementation and modification easy. It also

avails a generalisation accuracy that is

favourable on many domains (Duda, Hart,

2000).

Disadvantages

One disadvantage of this algorithm is that it has

large memory space requirements. It needs to

store all the data and hence the need for large

memory space. Secondly, during instance

classification, all training occurrences have to be

visited and this makes the algorithm slow during

this procedure. A third disadvantage is that the

algorithm is easily fooled by irrelevant data

attributes; this means that the accuracy

decreases with increase in irrelevant data

attributes. Also, the fact that the accuracy

decreases with increased noise in the set of

training data is another disadvantage. Another

shortcoming of this algorithm is its

computational complexity [Witten, 2005]. This

is due to its intensive computational recall

(Duda, Hart, 2000). This algorithm is a

supervised learning and this means it runs

slowly. The algorithm is highly vulnerable to the

dimensionality curse and lastly, it is highly

biased by the value of k.

Naive Bayes classifier

Advantages

Naive Bayes classifiers have very simplified

assumptions and naive design and hence easy to

build. The structure is basically the same and

this eliminates the structure learning procedure.

Model building is highly scalable and it is

possible to parallelize scoring no matter the

algorithm. Secondly, these classifiers have

worked favourably in numerous real world

scenarios. It has outperformed many complex

classifiers in on large numbers of datasets,

despite its simplicity (Box, Tiao 1992). Thirdly,

this classifier requires a small amount of training

data to approximate parameters required for

classification which include averages and

variances of these variables. A fourth advantage

is that, it is only the class variances that need be

resolved and not the variance of the entire

dataset (Witten, Frank 2003). Both binary and

multiclass classification problems can be solved

by this algorithm. This algorithm relies on basic

probability rules making it simple in operation;

also being probabilistic the results are presented

in a form favourable for incident management

policy. Lastly, it facilitates a broader set of

model parameters to be used.

Disadvantages

The assumption that every variable is

independent of others is sometimes a problem.

The class estimates are sometimes absurd and

the threshold must be harmonized not set

analytically. This classifier has been

outperformed by newer approaches such as

boosted trees. It lacks the ability to solve more

complex problems in classification. Naive

Bayes algorithm is used in Bayesian spam

filtering and it is vulnerable to Bayesian

Poisoning (Box, Tiao 1992). Also the spam filter

is beaten by replacing text with pictures.

Another disadvantage is that if parameter

estimates are improved, the effectiveness of

such a classification will be affected. When

using the Bayesian filter, a user specific database

must be consulted on every message (Witten,

Frank, 2003). This database contains word

probabilities that are used to detect spam. Lastly,

the initialisation of the naive Bayes based filter

is a bit time consuming.

Works Cited

Bishop, Christopher. Pattern recognition and

Machine Learning. New York, NY: Springer,

Box, G., and Tiao, G. Bayesian Inference in

Statistical Analysis. New York. John Wiley &

Sons, 1992.

Duda, Richard; Hart Peter; Pattern

classification, 2nd ed. David Stork. 2000.

Frank, Eibe; Holmes, Geoffrey; and Witten,

Ian. Naive Bayes for regression. Machine

Learning, 2003.

Jensen, Finn. An Introduction to Bayesian

Networks. New York, NY: Springer-Verlag,

1996.

Jensen Finn; Thomas Graven. Bayesian

Networks and Decision Graphs. New York, NY:

Springer, 2007.

Gray, Robert. Entropy and Information

Theory. New York, NY: Springer-Verlag, 1990.

Cormen, Leiserson, and Rivest,

Ronald. Introduction to Algorithms. Cambridge.

MIT Press, 1990.

Quinlan, J. R. C4.5: Programs for Machine

Learning. Morgan Kaufmann Publishers, 1993.

Riel, Arthur. Object oriented Design

Heuristics. Addison Wesley. 1996.

Witten, Ian. Data Mining: Practical machine

learning tools and techniques. Morgan

Kaufmann, San Francisco, 2005.

Witten, Ian; Frank Eibe. Data Mining: Practical

machine learning tools and techniques with java

implementations. Morgan Kaufmann, San

Francisco, 2000.

Summary of C4.5 Algorithm: Data Mining

C4.5 algorithm is a decision tree with unlimited

number of paths within the node. This algorithm

can work only with discrete dependent attribute,

that is why it can solve only classification tasks.

C4.5 algorithm is considered to be one of the

most famous and widely used algorithms of

generating decision trees. It is necessary to

follow the next demands for working with C4.5

algorism:

1. Each record from set of data should be

associated with one of the offered classes,

it means that one of the attributes of the

class should be considered as a class mark.

It may be concluded that all the samples

should belong to the same class, otherwise

the mistakes are inevitable.

2. Each class should be discrete. Each sample

should belong to one of the classes.

3. The number of classes should be much

fewer from the number of samples in the

considered scope of data.

One should understand that C4.5 algorithm

works slowly with very large scale set of data.

Using the concept of information entropy, C4.5

builds the decision trees based on the set of data,

like ID3 algorithm. Filestem.ext is the form for

the files which are read and written within C4.5

algorithm (filestem is a file name, and ext is a

file extension which is aimed at defining the file

type). Working with the program, one is

expected to have at least two files, the first one

is with the file name and class definition and the

second one is with the date which gathers the set

of objects described by the value of the class

attributes. Considering the structure of a

decision tree based on C4.5 algorithm, it may be

either a leaf, which is predicted to identify a

class or a decision node with a number of

branches and sub trees, which show the possible

outcome of the trial (Quinlan 5).

There are two ways how this algorithm can

generate decision trees, batch mode and iterative

model. Batch mode (often called default mode)

generates a single decision tree. This tree covers

all the data available for the decision. Another

kind of this algorithm, iterative mode, is based

on the random basis. The set of data is selected

randomly. Then, a decision tree is generated

with adding some specific objects which have

been misclassified.

The actions are repeated and the decision tree is

continued until it is classified in a correct way or

it is found out that there is no any progress.

Keeping in mind that iterative model is based on

the subset selected randomly, many trials may be

used for generating decision trees based on the

same data. Keeping in mind that there can be

many different decision trees due to the multiple

trials, the presence of the filestem.unpruned is

necessary. This file is created with the purpose

to collect the decision trees in the process. If the

similar data is used for generating decision trees,

the latest variant of the tree is used. The machine

saves the best generated decision tree in the file

filestem.tree.

“Data Mining and Customer Relationship

Marketing in the Banking Industry“ by Chye

& Gerry

Summary of the Article

The article under consideration, written by Chye

and Gerry (2002), focuses on data mining as a

part of newly emerging tools for the business

assessment processes. The authors state that data

warehousing and knowledge management are

other components of new information systems

(Chye & Gerry, 2002). According to the article,

data mining aims to identify “valid, novel,

potentially useful, and understandable

correlations and patterns in data” (Chye &

Gerry, 2002, p. 1). It could be stated with

certainty that the advancement of computer

technologies, both in terms of hardware and data

mining software improvements, significantly

facilitates the accessibility and affordability of

data mining for different business companies

(Chye & Gerry, 2002). The article is subdivided

into five parts, which explore various aspects of

data mining in the context of customer

relationship management. This paper aims to

summarize each of the article’s sections

subsequently.

Customer Relationship Management

First of all, the article generally elaborates on the

notion of customer relationship management

(CRM), which is defined as “the process of

predicting customer behavior and selecting

actions to influence that behavior to benefit the

company” (Chye & Gerry, 2002, p. 2). Further,

the article states that there are several main

objectives of CRM. The first objective is to get

closer to the customer by utilizing the data from

the enterprise databases (Chye & Gerry, 2002).

Thus, a company can predict the customer’s

behavior in advance (Chye & Gerry, 2002).

Secondly, it is essential for companies to

become more customer-oriented. This goal is

reached by a greater focus on customer

profitability rather than line profitability (Chye

& Gerry, 2002). Other objectives include better

customer response and loyalty, more efficient

lead management, and increased cross-selling

possibilities.

Data Mining Methodology

According to the authors, data mining is a

relatively recent practice in the business sphere

since it emerged in 1994 (Chye & Gerry, 2002).

There are five primary stages of data mining:

sampling, exploring, modifying, modeling, and

assessing the gathered data (Chye & Gerry,

2002). Sampling is required when data is too

voluminous or it is needed to avoid problems of

generalization (Chye & Gerry, 2002).

Exploration and modification stages refer to the

processes of understanding the information and

developing meaningful insights. In the modeling

stage, the actual analysis is performed through a

set of different approaches, such as traditional

statistical methods, neural networks, decision

trees, etc. (Chye & Gerry, 2002). It should be

stated that data mining tools are considerably

variable and numerous, and thus they are

traditionally categorized into three groups

according to their purpose: “description and

visualisation, association and clustering,

classification and estimation” (Chye & Gerry,

2002, p. 4). Overall, the implementation of data

mining gives the company a competitive

advantage.

Banking Applications

There is a considerable amount of academic

literature and research dedicated to the use of

data mining in business. For example, the

authors state that, according to the professional

and trading literature, numerous companies use

data mining to be more competitive in their

industries (Chye & Gerry, 2002). Considering

the application of data mining to banking, it is

possible to mention several examples. Firstly,

data mining could be used by banks as a part of

their risk management, namely operating the

credit risks (Chye & Gerry, 2002). Another

sphere where this approach is used is customer

acquisition (Chye & Gerry, 2002). The

employment of enterprise databases makes it

possible to predict the customer’s response to

the bank’s marketing campaigns.

Application to Churn Modelling

One of the most evident examples of using the

data mining methodology is its application to

churn modeling. The authors propose a fictional

situation where it is needed to consider a

customer relation application (or churn

modeling) for a fictitious banking company,

ZBANK (Chye & Gerry, 2002). ZBANK faces

increasing competition on the market due to

customer defections (Chye & Gerry, 2002).

Through the process of implementing various

data mining tools, it is identified that the

decision tree model is capable of predicting the

number of customers who are inclined to the

voluntary churn (Chye & Gerry, 2002). Overall,

it should be stated that data mining, with the

employment of demographic and transactional

information, can accurately identify churners

and non-churners.

Data Mining Limitations

Considering the limitations of this approach, it

is possible to mention three primary aspects.

Firstly, the authors state that some products of

random fluctuations will certainly emerge

during efficient exhaustive mining of data (Chye

& Gerry, 2002). This limitation is particularly

true in the context of big data set with different

variables. Secondly, the data mining method is

well-designed for modeling, but it does not show

the same efficiency with the assessment of the

results (Chye & Gerry, 2002). Finally, the

application of data mining tools requires both

profound pieces of knowledge of the sphere to

which the method is applied and proficiency in

data mining (Chye & Gerry, 2002).

Critiques for the Paper

It should be noted that the article is of

considerably high academic quality. It provides

a profound overview of data mining

methodology in the context of customer

relationship management. The article is highly

applicable to various spheres of business, and

thus it would be useful for a wide range of

businessmen and managers. One of the primary

strengths of the article is that the authors provide

a vast amount of information about data mining,

supporting their claims with the evidence from

academic literature and real-life examples of

banks, which successfully employ the

methodology. Among the weaknesses of the

article, one can identify that it is primarily

focused on banking applications, while other

industries are not given enough insights on the

implementation of data mining.

Research

Further, regarding possible recommendations

for the development of more valuable research,

one can state that the article’s primary weakness,

which is mentioned in the previous section,

should be the main reference point for new

research. Particularly, it means that the authors

should focus more on the applications for other

business spheres. It would bring additional value

to the paper. Also, since the authors are primarily

focused on the banking industry, it would be

considered beneficial to add an application of

data mining methodology to a real-life banking

situation rather than a fictional one.

Further Additions to the Paper

Considering future work that I may add to the

paper, I should state that it will be a significant

improvement for the paper if a more profound

literature review is given. As it was already

mentioned, the authors employed a considerably

vast number of sources; however, they did not

provide specific information on the topics of

customer relationship management and risk

management. In my opinion, these two aspects

of managerial work are immensely important for

any company, especially in the banking industry

since various risks are involved.

Other Academic Sources on the Topic

Also, it is essential to mention two academic

sources, which explore the topics related to the

scope of the research under consideration. The

first source is an article, written by Khodakarami

and Chan (2014). It is chosen because it explores

the topic of customer relationship management

more profoundly, and it also gives profound

insights on the employment of data mining tools

in CRM. The second article by Chen, Deng,

Wan, Zhang, Vasilakos and Rong (2015)

investigates the peculiarities of the

implementation of data mining methodology to

the Internet of Things, which is a considerably

perspective scope of the research.

Data Mining Techniques and Applications

Select two application areas for data mining

NOT discussed in the textbook and briefly

discuss how data mining is being used to solve

a problem (or to explore an opportunity)?

Data mining involves rearranging large volumes

of data to create comprehensible information

that can be used to solve problems. There are

several ways in which data mining can be

applied in the real world (Han et al. 76). It can

be used to solve problems and explore

opportunities.

Data Mining and the Detection of

Disturbances in the Ecosystem

The use of data mining to detect disturbances in

the ecosystem can help to avert problems that

are destructive to the environment and to

society. Such calamities include floods and

droughts (Kumar and Bhardwaj 258). Remote

sensing and earth science techniques are used to

understand the radical changes in the

environment. Data is collected and archived. It

is later mined and used to detect disturbances.

Data Mining in Sports

Data mining can be used to predict sporting

activities. A case in point is the Advanced Scout

System developed by IBM (Leung and Kyle

715). The application is used by coaches to

improve the performance of players. In most

cases, fans predict games by watching. They

may also use archived data, which is mined and

statistically used to make predictions based on

the history of the game.

What is Association Rule Mining? And

explain how Market-basket analysis helps

retail business to maximize the profit from

business transactions?

Association Rule Mining

It is the retrieval of data based on the

relationship between a given set of objects. It

takes into consideration the ‘togetherness’ of

these objects and how they appear in a database.

It involves the identification of connections and

correlations between objects (Ramageri 304).

Market-Based Basket Analysis and Retail

Business

Market basket analysis and association rule

mining can be used to maximize profits and

improve transactions in the retail business. It is

used to study the behavior of customers and their

shopping trends. Marketers use the information

to design catalogs and undertake customer

behavior analysis (Han et al. 99). Consequently,

the information can be used in marketing and

advertisement to maximize profits and improve

business transactions.

Discuss k-Nearest Neighbor (KNN) learning

algorithm. What is the significance of the

value of k in k-NN?

K-Nearest Neighbor (KNN) Learning

Algorithm

The algorithm is a method that is used to classify

data obtained from sources with similar sets of

parameters. It uses a set of data based on the

known classifications of the existing database. It

makes use of separate classes to predict a new

pattern and classify the new data. The

‘neighbors’ in this case are the separate sets of

data with common characteristics (Bhatia and

Vandana 304). For instance, a bank may get a

customer who wants a loan, but the entity lacks

time to calculate the credit rating of the

applicant. The bank can use previous credit

ratings of people with similar characteristics,

such as earnings and collaterals.

The Significance of the Value of k in k-NN

The k represents the number of classes used in

the comparison. Lower values of this component

are more accurate compared to higher values.

On the other hand, increasing the random data

point raises the percentage error of

approximation (Bhatia and Vandana 304). As

such, k can be used to obtain the most accurate

approximation in data classification and

regression.

Discuss the two estimation methods of

classification-type data mining models while

considering ANN as a classifier

Supervised Learning

It is one of the estimation methods of

classification data mining models in artificial

neural networks (ANN). In this case, a set of

example pairs is provided. The objective is to

identify or ‘estimate’ a function. The function

has to lie within the permitted cluster of

functions (Nikam 15). In addition, it has to

reflect the given examples.

Unsupervised Learning

In this estimation method, the ANN works with

a given set of data. The data is usually denoted

as x. The cost function to be minimized is also

provided. The latter can be a random function

of x. It can also be the output of the network. The

output is usually denoted as f. The cost function

relies on what the network is trying to model

(Nikam 16). It is also affected by the

assumptions made.

Works Cited

Bhatia, Nitin, and Ashev Vandana. “Survey of

Nearest Neighbor Techniques.” International

Journal of Computer Science and Information

Security, vol. 8, no. 2, 2010, pp. 302-305.

Han, Jiawei, et al. Data Mining: Concepts and

Techniques. 3rd ed., Morgan Kaufmann

Publishers, 2011.

Kumar, Dharminder, and Deepak Bhardwaj.

“Rise of Data Mining: Current and Future

Application Areas.” International Journal of

Computer Science Issues, vol. 8, no. 5, 2011, pp.

256-260.

Leung, Carson, and Joseph Kyle. “Sports Data

Mining: Predicting Results for the College

Football Games.” Procedia Computer

Science, vol. 35, 2014, pp. 710-719.

Nikam, Sagar. “A Comparative Study of

Classification Techniques in Data Mining

Algorithms.” Oriental Journal of Computer

Science & Technology, vol. 8, no. 1, 2015, pp.

13-19.

Ramageri, Bharati. “Data Mining Techniques

and Applications.” Indian Journal of Computer

Science and Engineering, vol. 1, no. 4, 2011, pp.

301-305.

E-Commerce: Mining Data for Better

Business Intelligence

Abstract

Business enterprises operate in competitive

environments, which compel them to use data

mining methodologies to convert unstructured

data into information for business intelligence.

The data consisting of millions of transactions is

used to fragment existing and target markets

according to consumer behavior.

Here, the business intelligence information

gained from data mining is also used to detect

fraud, understand consumer behavior, gain

deeper insight into business operations, and to

benchmark business operations based on data

classification, prediction, and association rules.

Introduction

Many organisations struggling to remain

competitive in the market use data mining to

gain business intelligence information. Business

intelligence is important for forecast demand

management, supply chain management, cost

management, category management, and to

exploit existing business intelligence

opportunities to improve the core business

processes (Giudici 69).

For instance, Intel uses data mining to

strategically access business intelligence

information, which yields accurate information

for effective decision making to enhance value

creation opportunities at different strategic

levels of the organization (Giudici 89).

Literature review

Many organizations have established that data

mining is a new powerful tool that has been

integrated into the business practices of different

firms for analyzing large amounts of

unstructured data to discover new and emerging

trends in the fiercely competitive markets. Here,

business organizations use automatic methods to

analyze data from large databases to establish

previously unknown patterns of data.

The amounts of business data, which is

generated from millions of transactions, cannot

be processed promptly using traditional

mechanisms. However, with the advent of new

technologies, which have high processing

powers, large amounts of unstructured raw data

can be mined and processed in a short time form

large data warehouses (Delmater and Hancock

2).

Intel is one of the examples of firms, which mine

data from different data warehouses to evaluate

the company’s performance by benchmarking

her business processes against successful

business practices. The tangible benefits that

accrue from lintel’s business processes include

the elimination of unnecessary costs using

analytical reports to determine the performance

and trend of the business.

The data mining process consists of data

classification, prediction, and association rules.

To ensure that the data, which is mined for

business intelligence is used appropriately to

meet Intel’s business goals and objectives, Intel

first classifies unknown data according to

established classification rules. The process

includes identifying 90% of the portions of data

of the organisation, which has not been

classified for current and future use by ensuring

that the classification process factors the goals

or the purpose for, which the data to be used.

The classification process captures data from

different sources in different unstructured forms,

which when processes into information is used

to determine the economies of scale, switching

costs, business capital requirements, the degree

of fragmentation and concentration of the

business, the potential for globalisation,

bargaining power of suppliers and buyers,

threats of substitutes, business growth rates, the

cost advantages of the business, and the degree

of intensity of competition (Kudyba and

Hoptroff 34).

The data is derived from the millions of

transactions involving the suppliers, information

about the customers, and the day to day

transactions and operations.

Data mining for business intelligence

A case in point is Intel. Intel uses data mining to

shift through large amounts of data to establish

the relationship between data sets, anomalies in

business activities, significant business facts and

patterns, existing and emerging trends, and to

make decisions on the approach to use to make

sales.

In addition, it is used to determine product

ranges, establish and develop better marketing

strategies, and determine the strategic

approaches for creating customer royalties

(Sofaer 23).

Market segmentation is used by companies to

determine and classify customers with the same

buying behavior.

In addition, the data provides accurate

predictions of trends of loyal customers for

competitors to predict and avoid customer

churn, determine fraudulent transactions,

identify the most appropriate interactive

marketing strategies, establish the purchasing

trends of customers, and establish the

purchasing trends and behavior of the customers

(Larson 39).

Sources of data

Organisations look for different sources of data,

which is mined to address different business

needs in different environments and the typical

sources of data include website data mining,

journals, and e-commerce stores. Such data can

be precious when launching new products into

the market.

Problem statement

Many business organisations make inaccurate

decisions because they have not integrated data

mining tools to make real time and accurate

information to establish, predict, and determine

accurately the market segments, detect fraud,

and determine the marketing strategies for

effective decision making.

To address the problem, this study will analyse

the data mining processes, and its benefits,

which organisations use as a source of business

intelligence reports for accurate and effective

decision making.

Objectives

• Determine how organisations use data

mining as a tool to generate business

intelligence information

• Identify the data mining process that is

used by organisations to organise

unstructured data for decision making

• To determine areas where organizations

can use data mining for effective decision

making

Scope

The scope of the study is to cover study data

mining as a process, the areas where data mining

can be applied in business organizations, its use

in business organizations such as Intel, and the

sources of data that is used in data mining.

Research Methodology (Qualitative)

The method of inquiry used to achieve the

objectives of the study was qualitative research

method (Sofaer 45). The method allowed the use

of Intel and an example to build the study and

the literature on data mining for business

intelligence to analyze the findings.

Model

The research model consists of the process for

data mining, the use of a typical real-world

example, which use data mining to organize

unstructured data into information for business

intelligence as graphically presented below

The results of the qualitative study, which were

based on the objectives and scope of the study,

are discussed below.

Objective one

The results of the study showed that business

organisations use data mining for business

intelligence, which is crucial for determining the

current patterns of data consumer behavior.

Intel is one example of the companies, which

combine different technologies, architectures

and methodologies to mine data for business

intelligence, which is used for accurate decision

making, which is necessary for conducting cost

effective business transactions. In addition, the

study shows that Intel uses the data to

benchmark its business operations and

performance to determine the benefits that

accrue from its business operations.

Objective two

The data mining process that companies use to

accurately organise unstructured data into

business intelligence information includes

classifying the data into known and unknown

categories. The known category of data exists in

established patterns and the unknown category

of data has to be organised into specific patterns

using the rules that for classifying the data into

the known categories.

The prediction process is used to establish the

probability of the certain business trends such as

the changing buyer behavior according to the

dynamic business environment.

Gradually, the association rules are used to

determine what and why certain actions have to

be performed to achieve business goals and

objectives.

Objective three

Data mining provides business intelligence

information that can be used to make decisions

to determine the right market segments,

marketing strategies, to detect instances of

fraud, and determine the rate of customer

turnover to other business competitors (Howson

75).

Conclusions

The motivation for the study was to determine

the rationale for using data mining as a strategic

business tool for business intelligence. It was

established that data mining is a crucial tool that

competing organisations such as Intel use to

organise millions of unstructured business

transactions into information to influence

decision making.

The study showed that data mining can be used

to determine market competiveness, consumer

behavior, market segmentation, market trends,

marketing strategies, and customer movements.

The approach used automated predictions and

discovery mechanisms to organise unknown

patterns of data into information for decision

making. Here, the benefits from the study

included Information integration, enhanced

presentation capabilities, insight creation, better

organisational memory, benchmarking, and

effective decision making.

Works Cited

Delmater, Rhonda, and M. Hancock. Data

mining explained: a manager’s guide

to customer-centric business intelligence. New

York, Digital Press, 2001. Print.

Giudici, Paolo. Applied data mining: statistical

methods for business and industry. New York:

John Wiley & Sons, 2005. Print

Howson, Cindi. Successful business

intelligence. New Dell: Tata McGraw-Hill

Education, 2007.

Kudyba, Stephan, and R. Hoptroff. Data mining

and business intelligence: A guide to

productivity. New York: IGI Global, 2001. Print.

Larson, Brian. Delivering Business Intelligence

with Microsoft SQL Server 2005. New York,

McGraw-Hill, 2006. Print.

Sofaer, Shoshanna. “Qualitative research

methods.” International Journal for Quality

in Health Care 14.4 (2002): 329-336.Print.

Data Mining: A Critical Discussion Analytical

Introduction

In recent times, the relatively new discipline of

data mining has been a subject of widely

published debate in mainstream forums and

academic discourses, not only due to the fact that

it forms a critical constituent in the more general

process of Knowledge Discovery in Databases

(KDD), but also due to the increased realization

that this discipline can be applied in a number of

areas to enhance decision making processes,

efficiency, and competitiveness in contemporary

organizations (Kusiak, 2006).

The basic concept behind the emergence of data

mining, and which has contributed immensely to

its admissibility as one of the increasingly used

strategies in business establishments as well as

scientific and research undertakings, is that by

automatically sifting through large volumes of

information which may primarily appear

irrelevant, it should be possible for interested

parties to extract nuggets of useful knowledge

which can then be used to drive their agenda

forward (Adams, 2010).

Goth (2010) observes that the emergence of data

mining has been primarily informed by the rapid

growth in data warehouses as well as the

recognition that this heap of operational data can

be potentially exploited as an extension of both

business and scientific intelligence.

The present paper seeks to critically discuss the

discipline of data mining with a view to

illuminate knowledge about its origins,

concepts, applications, and the legal and ethical

issues involved in this particular field.

Definition & History of Data Mining

Although data mining as a concept has been

defined differentially in diverse mediums, this

report will adopt the simple definition given by

Payne & Trumbach (2009), that “…data mining

is the set of activities used to find new, hidden or

unexpected patterns in data” (p. 241-242).

The purpose of data mining, as observed by

these authors, is to extract information that

would not be readily established by searching

databases of raw data alone. Through data

mining, organizations are now able to combine

data from incongruent sources, both internal and

external, from across a multiplicity of platforms

with a view to assist in a variety of business

applications.

At its most elemental state, data mining utilizes

proved procedures, including modeling

techniques, statistical investigation, machine

learning, and database technology, among

others, to seek prototypes of data and fine

relationships in the sifted data with the main

objective of deducing rules and intricate

relationships that will inarguably permit the

extrapolation of future outcomes (Pain &

Trumbach, 2009; Adams, 2010).

Researchers and practitioners are in agreement

that the capability of both generating and

collecting data from a wide variety of sources

has greatly impacted the growth trajectories of

data mining as a discipline.

This capability, according to Adams (2010) and

Chen (2006), was precipitated by a number of

variables, which can be categorized into the

following:

1. increased computerization of business,

scientific, and government transactions

with the view to increase efficiency and

productivity,

2. extensive usage of electronic cameras,

scanners, publication devices, and

internationally recognized bar codes for

most business-related products,

3. advances in data gathering instruments

ranging from scanned documents and

image platforms to global positioning and

remote sensing systems,

4. the development and popularization of the

World Wide Web and the internet as

widely accepted global information

systems.

This explosive growth in stored or ephemeral

data brought us to the information age, which

was, and continues to be, characterized by an

imperative need to develop new techniques,

procedures and automated tools that can astutely

assist us in transforming and making sense of the

huge quantities of data collected via the above

stated protocols (Goth, 2010).

To dig a bit deeper into the history of data

mining, research has been able to establish that

the term ‘data mining’, which was introduced in

the 1990s, has its origins in three interrelated

family lines. It is important to note that the

convergence of these family lines to develop a

unique discipline in the context of data mining

certainly gives it its scientific foundation

(Adams, 2010).

This notwithstanding, extant research (Adams,

2010; Chez, 2006) demonstrate that the longest

of these family lines to be credited with the

gradual development of data mining as a fully-

fledged discipline is known as classical

statistics.

Researchers are in agreement that it would not

have been possible to develop the field of data

mining in the absence of statistics as the latter

provides the foundation of most technologies on

which the former is built, such as “regression

analysis, standard distribution, standard

deviation, standard variance, discriminant

analysis, and confidence intervals” (Goth, 2010,

p. 14).

All these concepts, according to this author, are

used to study data and data relationships –

central aspects in any data mining exercise.

The second longest family line that has

contributed immensely to the emergence of data

mining as a fully-fledged field is known as

artificial intelligence, or simply AI. Extant

research demonstrate that the AI discipline,

which is developed upon heuristics as opposed

to statistics, endeavors to apply human-thought-

like processing to statistical challenges while

using computer processing power as the

appropriate medium (Talia & Trunfio, 2010).

It is important to mention that since this

approach was tied to the availability of

computers and supercomputers to undertake the

heuristics, it was not practical until the early

1980s, when computers started trickling into the

market at reasonable prices (Goth, 2010).

The third family line to have influenced the field

of data mining is what is generally known as

machine learning or, better still, the

amalgamation of statistics and AI (Adams,

2010). Here, it is of importance to note that

while AI could not have been viewed as a

commercial success during the formative years,

its techniques and strategies were largely co-

opted by machine learning.

It is also important to note that machine learning,

while able to take the full benefit of the ever-

improving price/performance quotients

provided by computers in the decades of the

1980s and 1990s, found usage in more

applications because the entry price was lower

that that of AI, not mentioning that it was largely

considered as an evolved facet of AI as it was

effectively able to blend AI heuristics with

complex statistical analysis (Chen, 2006).

Review of how Data Mining is used Today

and how it could be used in the Future

Presently, there exist broad consensus that data

mining is mostly based on the machine leaning

techniques; that is, it is fundamentally perceived

as the adaptation of machine learning techniques

and concepts to a wide variety of areas, such as

business and scientific applications (Adams,

2010).

Therefore, the present-day data mining can only

be described as the amalgamation of historical

and recent developments, particularly in

statistics, artificial intelligence, and machine

learning, with a view to developing a software

program that can run on a standard computer to,

among other things, make diverse decisions

based on the data under study, use statistical

concepts and applications to establish various

relationships among the data, and also use more

advanced artificial intelligence heuristics and

algorithms to achieve its major goal (Talia &

Trunfio, 2010).

Extant research demonstrate that the major

objective of current data mining applications is

to sift through huge volumes of data to extract

nuggets of useful data, which can then be used

to establish previously-hidden trends or patterns.

Today, more than ever before, data mining is

used in the business arena to boost corporate

profits by improving customer relations and

targeting new customers (Cary et al, 2003).

According to these authors, “…AT&T Wireless

was able to increase it’s subscriber base by 20%

in less than a year when it contracted with a data-

mining company to identify customers that

would likely to be interested in AT&T’s new

flat-fee wireless service” (p. 158).

The AT&T story demonstrates that visions of

achieving good returns continue to drive

businesses toward embracing data mining

technology.

Data mining is bound to be used along the same

lines in the future to enable enterprises make

critical decisions from a knowledge-oriented

perspective. Consecutive studies have

demonstrated that most business organizations

fail to wade through the harsh economic waters

of modern times due to their perceived

inadequacy to base their most important

decisions on knowledge and evidence (Adams,

2010; Goth, 2010).

However, it is now evident that data mining can

be used to endear organizations closer to a

knowledge-based economy, which basically

translates into the use of knowledge to generate

economic benefits.

Chen (2006) observes that a knowledge-based

economy necessitates data mining processes to

become more goal-oriented with the view to

generating an enabling environment where more

tangible results can be achieved.

Consequently, data mining should be used in the

future not only to facilitate the uncovering of

concealed knowledge beneath the ocean of data

readily found in a multiplicity of mediums and

applications, but also to ensure that it makes

important contributions to the knowledge-based

economy with the express intention of coming

up with more tangible business and scientific

outcomes (Chen, 2006; Adams, 2010).

Types of Data Mining Applications

There exist a multiplicity of data mining

applications which can be used in diverse

situations and environments depending on the

major objective for usage. Some data mining

applications, according to Chen (2006), are

simple to use and may be offered for free, while

others are complex and require a sizeable

investment to operationalize.

This section will discuss some data mining

applications based on the sector of practice, and

will mainly focus attention to the banking and

finance, retail, and the healthcare sectors of the

economy.

Data Mining Applications in the Banking &

Finance Sector

Most banking institutions have over the years

employed a multiplicity of data mining

applications to model and predict credit fraud, to

assess borrower risk, to undertake trend

analysis, and to evaluate profitability, as well as

to assist in the initiation and management of

direct marketing activities (Seifert, 2004).

In equal measure, most finance and credit

companies have over the years employed a

variety of neural networks and other data mining

applications “…in stock-price forecasting, in

option trading, in bond rating, in portfolio

management, in commodity price prediction, in

mergers and acquisitions, as well as in

forecasting financial disasters” (p. 191).

Here, it can be noted that the Neural

Applications Corporation has developed an

effective application known as NETPROPHET,

which is increasingly being used by finance

companies to make stock predictions by

illustrating the real and predicted stock values

depending on the type of data that has been

keyed into the system (Groth, 1999; Chen,

2006).

The banking sector is continuously been faced

with fraud cases, and data mining applications

such as HNC Falcon has assisted the institutions

to monitor payment-card applications,

decreasing fraud cases by almost 75 percent

while increasing applications for payment card

accounts by as much as 50 percent on a yearly

basis (Groth, 1999).

The importance of banks to develop data mining

applications that could be used in cross-selling

and maintenance of customer loyalty has been

well documented in literature.

These applications, according to Groth (1999),

mainly assist banking institutions to model the

behavior of their customers in such a manner

that the resulting relationships could be used to

establish the needs and demands of their

customers, as well as make objective predictions

into the future.

RightPoint software, Security First, and

BroadVision are some of the vendors primarily

interested in integrating predictive technologies

with consumer interaction points to ensure

customer needs and demands are efficiently

dealt with (Groth, 1999; Chen, 2006), in

addition to using predictive technologies to

integrate one-to-one marketing strategies to

their clients banking sites (Adam, 2010).

According to Groth (1999), “…the RightPoint

Real-Time Marketing Suite takes data-mining

models and leverages them within real-time

interactions with customers” (p. 194). This

application is unique in that it is designed to

develop, manage and deliver one-to-one

marketing initiatives for high-end industries that

heavily depend on direct customer interaction to

undertake business (Goth, 2010).

As a general prerogative, it is important to note

that majority of the data mining applications

used in the banking and finance sector attempt

to ensure that each customer interaction seizes

the prospect of enhancing customer satisfaction,

loyalty, motivation, and profit-generation

potential (Talia & Trunfio, 2010; Zhang &

Segall, 2008).

Data mining Applications in Retail

Intense competition and slim profit margins

have obliged retailers to embrace data

warehousing strategies earlier than other sectors.

As observed by Groth (1999), “…retailers have

seen improved decision-support processes lead

directly to improved efficiency in inventory

management and financial forecasting” (p. 198).

It is a well known fact that expansive retail and

supermarket chains are in possession of huge

quantities of point-of-sale data that is not only

information-rich, but could be employed using

appropriate data mining applications to improve

the stated decision-support strategies, improve

efficiency in financial predictions and inventory

management, and analyze customer shopping

patterns (Seifert, 2004).

In the retail sector, the AREAS Property

Valuation product from HNC software, as well

as SABRE Decision Technologies, serves as

good examples on how data mining applications

can be used in the retail sector to perform

valuations, projection and forecasting, customer

purchasing behavior analysis, and customer

retention analysis, with the underlying purpose

of increasing profitability, enhancing customer

experience, and making better and more

informed business decisions (Zhang & Segall,

2008).

In evaluating customer profitability in the retail

sector, a software vendor referred to as Dovetail

Solutions has developed a data mining

application known as Value, Activity, and

LoyaltyTM (VALTM), with a view to utilize

transactional business data from the retailers to

synthesize information about customer activity

and processes, churn rate, and anticipated future

purchases (Groth, 1999; Chen, 2006).

Data Mining Applications in the Medical

Field

The vast amount of data available within the

healthcare industry, including the associated

data collected via medical research, biotechs,

and the pharmaceutical industry, have provided

a fertile ground for data mining applications to

grow. The knowledge that data mining has been

employed expansively in the medical industry is

in the public domain.

For example, we are aware that the vendor

NeuroMedical Systems ingeniously employed

neural networks to create a pap smear diagnostic

aid, while both Vysis Rochester Cancer Center

and the Oxford Transplant Center continues to

employ a data mining application known as

KnowledgeSEEKER, which utilizes a decision

tree technology, to assist in various research

undertakings (Groth, 1999; Adams, 2010; Chen,

2006).

It is important to note that these applications are

beneficial in the medical sector as they enable

health practitioners to come up with accurate

diagnosis even without subjecting patients to

physical examination (Koh & Tan, 2008).

Governments and other interested health

agencies can utilize data mining applications,

such as MapInfo, KnowledgeSEEKER, and

LEADERS, among others, to: demonstrate

average costs of health services; show efficiency

of a particular prescription over time; reveal

efficacy rates of diverse pathogens over time;

develop superior diagnosis and treatment

protocols; show patient location in order to

deliver superior health services; or assist

healthcare insurers to detect fraud (Koh & Tan,

2008; Wen-Chung et al, 2010).

Legal & Ethical Issues in Data Mining

As is the case in other disciplines, the field of

data mining is faced with a complexity of legal

and ethical issues which needs to be addressed

for the applications to succeed.

In the legal arena, it is important to evaluate how

organizations should employ data mining

applications while remaining focused on

protecting the private information of their

customers so as to avoid customer

dissatisfaction or even being subjected to legal

action by customers who may feel that the

organizations intruded into their privacy (Cary

et al, 2003; Wen-Chung et al, 2010).

In terms of ethical issues, it is a well known fact

the spread of personal information, as is the case

in many data mining applications, can lead to

elevated risks of customer identity theft (Cary et

al, 2003).

According to Payne & Trumbach (2009), data

mining processes brings into the fore a scenario

where “…the consumer loses aspects of privacy

as all of their basic demographic information,

personal interests, correspondence and activities

are stored in databases and available to be

combined together” (p. 243).

Such a scenario has obvious ethical

ramifications since this information can be used

to the disadvantage of the customers.

Another ethical query arises from the fact that

consumers lose the control over what happens to

their personal information held in large

databases, implying that such kind of

information can be used to the disadvantage of

the providers if it happens to fall into the wrong

hands (Payne & Trumbach, 2009; Cary et al

2003; McGraw, 2010).

What’s more, customers who provide personal

information to organizations face a more

ominous challenge that may entail potential

discrimination based on the personal

information they either provide or refuse to

provide to the organizations.

Another important factor to consider when

evaluating ethical concerns in data mining is that

there is no fine line distinguishing if it is indeed

necessary for an organization to use the private

information of its customers to enhance its

profitability or if such information should be

sorely used to improve customer satisfaction and

maintain consumer trust (Payne & Trumbach,

2009).

Lastly, it is well known that a number of data

mining processes may yield incorrect

conclusions, which may be costly to the

organization as well as to the customers

(McGraw, 2010).

Conclusion

This discussion has brought into the fore

important aspects of data mining, its current and

future uses, as well as perceived limitations in

terms of legal and ethical constraints.

The general consensus among academics and

practitioners is that data mining represents the

new frontier of growth, particularly in nurturing

mutually fulfilling customer relationships,

ensuring that customer’s needs and demands are

satisfactorily met, and in facilitating

organizations to forecast and predict future

growth and decision patterns (Adams, 2010;

Kusiak, 2006; Wen-Chung et al, 2010).

The task is therefore for developers to continue

investing heavily in effective and efficient data

mining applications to ensure that such tools are

able to achieve what they were originally

intended to achieve. Consequently, research and

development into these applications and tools is

of primary importance.

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Data Mining Role in Companies

The increasing adoption of data mining in

various sectors illustrates the potential of the

technology regarding the analysis of data by

entities that seek information crucial to their

operations. Data mining tools enable entities to

establish relationships such as associations,

classes, clusters and sequential patterns.

The analysis of data can occur using a variety of

techniques such as genetic algorithms, rule

induction, neural networks and data

visualization. Information obtained through data

mining has transformed business operations by

aiding companies in decision-making and the

prediction of crucial factors such customer’s

behavior, which helps companies to gain a

competitive advantage.

Benefits of data mining to businesses

The generation of predictive scores for

organizational elements is crucial in the analysis

of the behavior of customers. Predictive

analytics enable companies to optimize

marketing strategies by modeling trends in

customers’ responses. This information helps

businesses to allocate funds for various

campaigns based on their potential of

succeeding.

In addition, it minimizes wastage of time and

money caused by the use of manually analyzed

marketing strategies. Research shows that

manual analysis of marketing methods is a

cumbersome process that is error-prone due to

the extensive skills required. Predictive

analytics eliminate guesswork in the

identification of marketing methods by

providing reliable data on customers’

preferences and habits (Pyle, 2003).

Analyzing past and present trends about

customers’ habits provides patterns that aid in

making decisions on future undertakings of a

business. A company that implements measures

in response to future patterns of customers’

behavior is likely to gain a competitive

advantage.

Data mining enables businesses to establish

relationships among items in a transaction. The

association technique facilitates identification of

products that customers purchase frequently.

Using the association rule, businesses can

determine the way one product influences the

sale of another product.

For example, an association of Bagels and

Potato Chips provides insight on products that a

fast foods business can sell with Bagels so that

the sale of Potato Chips increases.

Market based analysis provides businesses with

important information that guides the

implementation of marketing campaigns by

enabling them to establish hypotheses for

customers’ buying patterns. Relevant marketing

strategies boost sales and promote higher profits.

Web mining enables business entities to

establish patterns of customers’ behavior from

the web. Using data mining techniques,

companies can identify customers’ interests on

the web concerning textual or multimedia data

(Soares, 2010).

Web usage mining facilitates the analysis of

target demographics. Web content and

structured mining enable companies to monitor

brands and analyze the content and structure of

competitors. Such undertakings create strategic

advantages.

Clustering enables companies to identify

distinct groups of customers and implement

strategies to retain customers that are above a

cluster and gain the confidence and loyalty of

customers that are below the cluster.

Customer-relationship management uses

clusters to segment customers based on

particular variables indentified through data

mining. Variables such as customer-retention

probability help companies in the identification

of marketing opportunities.

Reliability of data mining algorithms

The reliability of data mining algorithms

depends on the nature of data under analysis.

Some datasets contain information that has

errors or is invalid. Research shows that

algorithms have diverse responses to errors and

thus compromise the results of data analysis in

different manners. Assumptions such as noise-

free data influence the accuracy levels of data

mining algorithms.

Another factor that interferes with the accuracy

of data mining algorithms is the size of data. The

search space varies depending on the

dimensions in a domain space.

Research shows that the relationship between

the search space and dimensions in a domain

space is exponential. This relationship

introduces a phenomenon known as curse of

dimensionality, which interferes with the

reliability of data mining algorithms

(Kantardzic, 2003).

Various errors arise due to factors that affect

data-mining algorithms. Systematic errors are

likely to arise due to assumptions on clean data.

Preprocessing of data helps to minimize such

errors. Other errors include training and

pessimistic errors that arise due to invalid data

and assumptions such as noise-free data.

Data mining infringement on privacy

Data for mining purposes raises many privacy

concerns. First, data intended for profiling

customers and analyzing their behavior contains

a lot of personal information. The collection and

storage of confidential information about

individuals introduces controversies due the

possibility of illegal access to the information.

Another issue concerns the dissemination of

implicit information about an individual or a

group of customers. Thirdly, data mining

discovers valuable information that is subject to

sale. This creates loopholes for the distribution

of confidential information without control.

To address the issue of privacy protection in data

mining, concerned bodies have established

measures that promote reliable data mining

results while meeting privacy requirements.

OECD Guidelines on data mining extensively

cover the use of personal information obtained

through data mining by providing various

guidelines (Aggarwal & Yu, 2008).

First, executors of data mining should clearly

inform subjects on the process and the intended

use of the collected data. This will ensure that

people participate under their free will.

Secondly, the Forthcoming Policy regulates the

use of data mining results by stipulating the

purpose of data, its allowed use, and persons

who should access the information. The

Disclosure policy enables subjects of data

mining to determine purposes for disclosure of

knowledge by giving or denying consent on the

anticipate use of data.

Businesses that have used predictive analysis

to gain a competitive advantage

Flight 540 employed the predictive analytics

strategy to boost their customer appeal and gain

competitive advantage.

They used data from the internet, customer

spending history, comment cards and various

surveys to model their flights in such a manner

that customers could easily make purchase

decisions depending on the flight packages on

offer. Instead of planning flights on a

standardized basis, the company modified them

as per groups of customers with similar

preferences.

Netflix became a key player in the video rental

business by using predictive results on customer

preferences to model a customer-centric

business approach. The company introduced

products such as video rentals that did not

constitute a lateness fee, and the provision of

video streaming based on customers’ requests.

Apart from expanding its market share, the

company succeeded in reducing its promotional

expenses.

Tusk supermarket was able to increase its sales

considerably by using predictive analytics to

establish feasible pricing strategies using data on

customer survey feedback. The data facilitated

estimation of price sensitivity and the

determination of price ranges that would have

minimal impacts on sales. This promoted

customer retention for the supermarket, increase

market share, and stabilized generation of

revenue.

Conclusion

Data mining provides companies with a basis for

analyzing customers’ behavior and responses of

potential customers by enabling them to

establish relationships among internal and

external factors of business operation. In this

regard, companies can effectively determine the

role of factors such as price, competition and

demographics in influencing customers’

behavior.

Commercial Uses of Data Mining

Data mining is a computer-based classification

and summarization of a large set of data into

grouped information based on the correlated

data. The data mining tools present the

information in a manner that is to interpret and

predict. Data mining process entails the use of

large relational database to identify the

correlation that exists in a given data.

Data used in this process is drawn from different

sources that are related to the outcome of the

target phenomenon (Chattamvelli 18). Experts

use data mining software to predict the

occurrence of various situations and events

(Miner 35). This paper analysis how data mining

is used commercially to earn money, optimize

performance and improve service delivery.

Business organizations use data mining

technology to improve business management.

These organizations use customized data mining

applications to generate business trends and

patterns from their vast relational database. The

applications have specialized inbuilt software

algorithm that performs correlation detection.

The principal role of the applications is to sift

the data to identify correlations. Eventually, the

software presents the correlations as statistical

trends and patterns. Various business

departments use data mining for management as

follows (Chattamvelli 27).

Marketing departments use data mining to

perform market analysis. Market analysis is the

process of monitoring changes in marketing

pattern and trends. Therefore, marketers use data

mining applications to investigate the possible

causes of changes in marketing trends (Miner

53). Research asserts that common changes in

market analysisss include customer complaints,

sales decline, and loss of customer’s loyalty.

In addition, marketing departments use market

analysis to monitor new products launched in the

market. Specialized data mining applications

display customer buying trends, post purchasing

behavior, customer reviews, and demand trends

among others. Marketers use the prevailing

market trends to identify the best promotional

strategy that will outdo the competitors

(Rahman 68).

Marketing departments also use data mining

technology to facilitate direct and interactive

marketing. Direct marketing is the process of

developing a comprehensive business mailing

list. The mailing list contains the addresses of

the all the business stakeholders (Han & Kamber

56).

Business experts assert that direct marketing is

one of the most efficient ways of winning

customer and supplier’s loyalty. On the other

hand, interactive marketing is the process of

optimizing the business web accessibility. The

process ensures the business website provides

the user with satisfactory information.

Successful interactive marketing leads to

improved customer loyalty and online purchases

(Soares & Ghani 58).

Customer care departments use customer data

mining applications to improve service delivery

on customer relations. The main function of

these applications is to automate customer

handling process to minimize response time.

Some advanced data mining tools have

automated inbuilt emailing feature that

automatically respond to general customer

concerns (Miner 65). Marketing departments

also use this feature to promote direct marketing.

Furthermore, the departments use specialized

data mining applications that automate

answering of the frequently asked questions

(FAQ). FAQ tool provides customers with a

quick online guide of solving various problems

without visiting the customer care. This practice

improves service delivery leading to improved

customer loyalty (Soares & Ghani 67).

Customer care department also uses customized

data mining applications to manage business

promotions. Promotional data mining

applications automate the process of selecting

winners in uplift modeling promotions (Rahman

45). Furthermore, the departments use data

clustering applications to automate customer

segmentation analysis. Market segments are

distinct groups of customers who buy similar

goods or services.

Market segmentation process helps the business

in identification of the most profitable customers

group for promotions and marketing. In

addition, customer care departments heavily rely

on data mining software for catalogue

marketing. The departments manage huge

database that host profiles of all other

departments in the organization. Therefore, the

department uses product profiles at their

disposal to produce product catalogues for sales

promotion (Han & Kamber 77).

Commercial companies widely used data

mining analysis in the human resource (HR)

department. HR departments use customized

applications to monitoring and optimize

employee’s performance. The application

provides information on employees working

history.

This information is essential in planning for

recruitment, promotion, training, and rewarding

(Miner 84). Nevertheless, HR department use

Strategic Management application to generated

key performance indicators (KPI). KPIs analysis

provides business management with a

performance progress report. The report

provides a summary of the possibility of

achieving the organization’s goals. Therefore,

production and sales managers use KPIs to

correct performance decline hence improving

the profit margin (Rahman 73).

Entrepreneurs use data mining technology to

make money. For instance, software

development experts design and sell data mining

software to companies, businesses, and

institutions. Research reveals that hundreds of

entrepreneurs have become millionaires from

the sale of data mining software. In addition, the

entrepreneurs does not require established

companies to sale their software.

Other entrepreneurs make money from data

mining by operating data mining agency. These

professionals use the available data mining

software to open a data mining consulting

agency. Later they approach institutions,

businesses and companies to help them run their

businesses. The agency charges consultation fee

at a favorable rate depending on the amount of

work and urgency (Rahman 80).

Business people can also use data mining

technology to save or make money. If the

business owners are experienced in data mining,

they can use this knowledge to analyze market

trends. From the analysis, they can predict

market threats like decline of price or loss of

market for their goods and services.

The business can use this opportunity to reduce

production or supply of services hence avoiding

losses. On the other hand, the business people

can predict a possible rise of demand of their

goods or services. In that case, the business

increases supply of its goods and services to

meet the expected demand. Research asserts that

businesses using this approach are highly

profitable since they always avoid losses and

maximize profits (Soares & Ghani 126).

Web and Search Engine Optimization (SEO)

designers use data mining to make money

online. Using reliable data mining software, the

designers identify the most researched keyword

for their website in different locations. The

designers use this keyword to optimize their

website content.

Such websites receive many organic visitors,

which is profitable for the businesses. Research

shows that most successful online selling

websites use this approach to generate traffic.

The method is also very profitable for SEO

experts who are paid on a commission basis.

When their website ranks high in the search

engines, they are likely to get more clicks or

signups hence earning more commission

(Rahman 142).

In conclusion, commercial organizations widely

use data mining as follows. Sales department of

commercial companies use data mining to carry

out customer churning. Customer churning

involves using the buying behavior to predict

whether the company is losing its customers to

its rivals. Management uses the customer

churning results to regulate production hence

averting losses. Marketing department uses data

mining technique to facilitate market analysis,

segmentation, maximizing profits and

minimizing expenditure.

Human resource department uses data mining

technology to oversee staff monitoring,

development and termination. Finally,

entrepreneurs have made a lot of money using

data mining software. In summary, use of data

mining is very instrumental for business

organization management and investment

(Soares & Ghani 158).

Chattamvelli, Rajan. Data mining algorithms.

Oxford: Alpha Science International, 2011.

Print.

Han, Jiawei, and Micheline Kamber. Data

mining: concepts and techniques. 3rd ed.

Amsterdam: Morgan Kaufmann, 2012. Print.

Miner, Gary. Practical text mining and

statistical analysis for non-structured text data

applications. Waltham, MA: Academic Press,

2012. Print.

Rahman, Hakikur. Ethical data mining

applications for socio-economic development.

Hershey: Information Science Reference, 2013.

Print.

Soares, Carlos, and Rayid Ghani. Data mining

for business applications. Amsterdam: IOS

Press, 2010. Print.

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