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Analytics, Data Science and A I: Systems for Decision Support

Eleventh Edition

Chapter 4

Data Mining Process, Methods, and Algorithms

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Learning Objectives (1 of 2)

4.1 Define data mining as an enabling technology for business analytics

4.2 Understand the objectives and benefits of data mining

4.3 Become familiar with the wide range of applications of data mining

4.4 Learn the standardized data mining processes

4.5 Learn different methods and algorithms of data mining

Slide 2 is list of textbook LO numbers and statements

Learning Objectives (2 of 2)

4.6 Build awareness of the existing data mining software tools

4.7 Understand the privacy issues, pitfalls, and myths of data mining

Slide 2 is list of textbook LO numbers and statements

Opening Vignette (1 of 3)

Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime

Predictive analytics in law enforcement

Policing with less

New thinking on cold cases

The big picture starts small

Success brings credibility

Just for the facts

Safer streets for smarter cities

Opening Vignette (2 of 3)

Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime

Discussion Questions

Why do law enforcement agencies and departments like Miami-Dade Police Department embrace advanced analytics and data mining?

What are the top challenges for law enforcement agencies and departments like Miami-Dade Police Department? Can you think of other challenges (not mentioned in this case) that can benefit from data mining?

Opening Vignette (3 of 3)

Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime

Discussion Questions (continued)

What are the sources of data that law enforcement agencies and departments like Miami-Dade Police Department use for their predictive modeling and data mining projects?

What type of analytics do law enforcement agencies and departments like Miami-Dade Police Department use to fight crime?

What does “the big picture starts small” mean in this case? Explain.

Data Mining Concepts and Definitions Why Data Mining?

More intense competition at the global scale.

Recognition of the value in data sources.

Availability of quality data on customers, vendors, transactions, Web, etc.

Consolidation and integration of data repositories into data warehouses.

The exponential increase in data processing and storage capabilities; and decrease in cost.

Movement toward conversion of information resources into nonphysical form.

Definition of Data Mining

The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases. -- Fayyad et al., (1996)

Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable.

Data mining: a misnomer?

Other names: knowledge extraction, pattern analysis, knowledge discovery, information harvesting, pattern searching, data dredging,…

Data Mining Is a Blend of Multiple Disciplines

Figure 4.1 Data Mining Is a Blend of Multiple Disciplines.

Application Case 4.1

Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining

Questions for Discussion:

What challenges were Visa and the rest of the credit card industry facing?

How did Visa improve customer service while also improving retention of fraud?

What is in-memory analytics, and why was it necessary?

Data Mining Characteristics & Objectives

Source of data for D M is often a consolidated data warehouse (not always!).

D M environment is usually a client-server or a Web-based information systems architecture.

Data is the most critical ingredient for D M which may include soft/unstructured data.

The miner is often an end user

Striking it rich requires creative thinking

Data mining tools’ capabilities and ease of use are essential (Web, parallel processing, etc.)

How Data Mining Works

D M extract patterns from data

Pattern? A mathematical (numeric and/or symbolic) relationship among data items

Types of patterns

Association

Prediction

Cluster (segmentation)

Sequential (or time series) relationships

Application Case 4.2

American Honda Uses Advanced Analytics to Improve Warranty Claims

Questions for Discussion:

How does American Honda use analytics to improve warranty claims?

In addition to warranty claims, for what other purposes does American Honda use advanced analytics methods?

Can you think of other uses of advanced analytics in the automotive industry? You can search the Web to find some answers to this question.

A Taxonomy for Data Mining

Figure 4.2 A Simple Taxonomy for Data Mining Tasks, Methods, and Algorithms.

Other Data Mining Patterns/Tasks

Time-series forecasting

Part of the sequence or link analysis?

Visualization

Another data mining task?

Covered in Chapter 3

Data Mining versus Statistics

Are they the same?

What is the relationship between the two?

Data Mining Applications (1 of 4)

Customer Relationship Management

Maximize return on marketing campaigns

Improve customer retention (churn analysis)

Maximize customer value (cross-, up-selling)

Identify and treat most valued customers

Banking & Other Financial

Automate the loan application process

Detecting fraudulent transactions

Maximize customer value (cross-, up-selling)

Optimizing cash reserves with forecasting

Data Mining Applications (2 of 4)

Retailing and Logistics

Optimize inventory levels at different locations

Improve the store layout and sales promotions

Optimize logistics by predicting seasonal effects

Minimize losses due to limited shelf life

Manufacturing and Maintenance

Predict/prevent machinery failures

Identify anomalies in production systems to optimize the use manufacturing capacity

Discover novel patterns to improve product quality

Data Mining Applications (3 of 4)

Brokerage and Securities Trading

Predict changes on certain bond prices

Forecast the direction of stock fluctuations

Assess the effect of events on market movements

Identify and prevent fraudulent activities in trading

Insurance

Forecast claim costs for better business planning

Determine optimal rate plans

Optimize marketing to specific customers

Identify and prevent fraudulent claim activities

Data Mining Applications (4 of 4)

Computer hardware and software

Science and engineering

Government and defense

Homeland security and law enforcement

Travel, entertainment, sports

Healthcare and medicine

Sports,… virtually everywhere…

Application Case 4.3

Predictive Analytic and Data Mining Help Stop Terrorist Funding

Questions for Discussion:

How can data mining be used to fight terrorism? Comment on what else can be done beyond what is covered in this short application case.

Do you think data mining, although essential for fighting terrorist cells, also jeopardizes individuals’ rights of privacy?

Data Mining Process

A manifestation of the best practices

A systematic way to conduct D M projects

Moving from Art to Science for D M project

Everybody has a different version

Most common standard processes:

C R I S P-D M (Cross-Industry Standard Process for Data Mining)

S E M M A (Sample, Explore, Modify, Model, and Assess)

K D D (Knowledge Discovery in Databases)

Data Mining Process: C R I S P-D M (1 of 2)

Cross Industry Standard Process for Data Mining

Proposed in 1990s by a European consortium

Composed of six consecutive steps

Step 1: Business Understanding

Step 2: Data Understanding

Step 3: Data Preparation

Step 4: Model Building

Step 5: Testing and Evaluation

Step 6: Deployment

Data Mining Process: C R I S P-D M (2 of 2)

Figure 4.3 The Six-Step C R I S P-D M Data Mining Process. 

The process is highly repetitive and experimental (D M: art versus science?)

Data Mining Process: S E M M A

Figure 4.5 S E M M A Data Mining Process.

Developed by S A S Institute

Data Mining Process: K D D

Figure 4.6 K D D (Knowledge Discovery in Databases) Process.

Which Data Mining Process is the Best?

Figure 4.7 Ranking of Data Mining Methodologies/Processes.

Source: Used with permission from KDnuggets.com.

Application Case 4.4

Data Mining Helps in Cancer Research

Questions for Discussion

How can data mining be used for ultimately curing illnesses like cancer?

What do you think are the promises and major challenges for data miners in contributing to medical and biological research endeavors?

Data Mining Methods: Classification

Most frequently used D M method

Part of the machine-learning family

Employ supervised learning

Learn from past data, classify new data

The output variable is categorical (nominal or ordinal) in nature

Classification versus regression?

Classification versus clustering?

Assessment Methods for Classification

Predictive accuracy

Hit rate

Speed

Model building versus predicting/usage speed

Robustness

Scalability

Interpretability

Transparency, explainability

Accuracy of Classification Models

In classification problems, the primary source for accuracy estimation is the confusion matrix

Estimation Methodologies for Classification: Single/Simple Split

Simple split (or holdout or test sample estimation)

Split the data into 2 mutually exclusive sets: training (~70%) and testing (30%)

For Neural Networks, the data is split into three sub-sets (training [~60%], validation [~20%], testing [~20%])

Estimation Methodologies for Classification: k-Fold Cross Validation

Data is split into k mutual subsets and k number training/testing experiments are conducted

Figure 4.10 A Graphical Depiction of k-Fold Cross-Validation.

Additional Estimation Methodologies for Classification

Leave-one-out

Similar to k-fold where k = number of samples

Bootstrapping

Random sampling with replacement

Jackknifing

Similar to leave-one-out

Area Under the R O C Curve (A U C)

R O C: receiver operating characteristics (a term borrowed from radar image processing)

Area Under the R O C Curve (A U C) (1 of 2)

Works with binary classification

Figure 4.11 A Sample R O C Curve.

Area Under the R O C Curve (A U C) (2 of 2)

Produces values from 0 to 1.0

Random chance is 0.5 and perfect classification is 1.0

Produces good a assessment for skewed class distributions too!

Classification Techniques

Decision tree analysis

Statistical analysis

Neural networks

Support vector machines

Case-based reasoning

Bayesian classifiers

Genetic algorithms

Rough sets

Decision Trees (1 of 2)

Employs a divide-and-conquer method

Recursively divides a training set until each division consists of examples from one class:

A general algorithm (steps) for building a decision tree

Create a root node and assign all of the training data to it.

Select the best splitting attribute.

Add a branch to the root node for each value of the split. Split the data into mutually exclusive subsets along the lines of the specific split.

Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached.

Decision Trees (2 of 2)

D T algorithms mainly differ on

Splitting criteria

Which variable, what value, etc.

Stopping criteria

When to stop building the tree

Pruning (generalization method)

Pre-pruning versus post-pruning

Most popular D T algorithms include

I D3, C4.5, C5; C A R T; C H A I D; M5

Ensemble Models for Predictive Analytics

Produces more robust and reliable prediction models

Figure 4.12 Graphical Illustration of a Heterogeneous Ensemble.

Application Case 4.5

Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People’s Healthcare Decisions

Questions for Discussion:

What did Influence Health do?

What were the challenges, the proposed solutions, and the obtained results?

How can data mining help companies in the healthcare industry (in ways other than the ones mentioned in this case)?

Cluster Analysis for Data Mining (1 of 4)

Used for automatic identification of natural groupings of things

Part of the machine-learning family

Employ unsupervised learning

Learns the clusters of things from past data, then assigns new instances

There is not an output/target variable

In marketing, it is also known as segmentation

Cluster Analysis for Data Mining (2 of 4)

Clustering results may be used to

Identify natural groupings of customers

Identify rules for assigning new cases to classes for targeting/diagnostic purposes

Provide characterization, definition, labeling of populations

Decrease the size and complexity of problems for other data mining methods

Identify outliers in a specific domain (e.g., rare-event detection)

Cluster Analysis for Data Mining (3 of 4)

Analysis methods

Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on.

Neural networks (adaptive resonance theory [A R T], self-organizing map [S O M])

Fuzzy logic (e.g., fuzzy c-means algorithm)

Genetic algorithms

How many clusters?

Cluster Analysis for Data Mining (4 of 4)

k-Means Clustering Algorithm

k: pre-determined number of clusters

Algorithm (Step 0: determine value of k)

Step 1: Randomly generate k random points as initial cluster centers.

Step 2: Assign each point to the nearest cluster center.

Step 3: Re-compute the new cluster centers.

Repetition step: Repeat steps 3 and 4 until some convergence criterion is met (usually that the assignment of points to clusters becomes stable).

Cluster Analysis for Data Mining - k-Means Clustering Algorithm

Figure 4.13 A Graphical Illustration of the Steps in the k-Means Algorithm.

Association Rule Mining (1 of 6)

A very popular D M method in business

Finds interesting relationships (affinities) between variables (items or events)

Part of machine learning family

Employs unsupervised learning

There is no output variable

Also known as market basket analysis

Often used as an example to describe D M to ordinary people, such as the famous “relationship between diapers and beers!”

Association Rule Mining (2 of 6)

Input: the simple point-of-sale transaction data

Output: Most frequent affinities among items

Example: according to the transaction data…

“Customer who bought a lap-top computer and a virus protection software, also bought extended service plan 70 percent of the time."

How do you use such a pattern/knowledge?

Put the items next to each other

Promote the items as a package

Place items far apart from each other!

Association Rule Mining (3 of 6)

A representative applications of association rule mining include

In business: cross-marketing, cross-selling, store design, catalog design, e-commerce site design, optimization of online advertising, product pricing, and sales/promotion configuration

In medicine: relationships between symptoms and illnesses; diagnosis and patient characteristics and treatments (to be used in medical D S S); and genes and their functions (to be used in genomics projects)

…

Association Rule Mining (4 of 6)

Are all association rules interesting and useful?

A Generic Rule: X  Y [S%, C%]

X, Y: products and/or services

X: Left-hand-side (L H S)

Y: Right-hand-side (R H S)

S: Support: how often X and Y go together

C: Confidence: how often Y go together with the X

Example: {Laptop Computer, Antivirus Software}  {Extended Service Plan} [30%, 70%]

Association Rule Mining (5 of 6)

Several algorithms are developed for discovering (identifying) association rules

Apriori

Eclat

F P-Growth

+ Derivatives and hybrids of the three

The algorithms help identify the frequent item sets, which are, then converted to association rules

Association Rule Mining (6 of 6)

Apriori Algorithm

Finds subsets that are common to at least a minimum number of the itemsets

Uses a bottom-up approach

frequent subsets are extended one item at a time (the size of frequent subsets increases from one-item subsets to two-item subsets, then three-item subsets, and so on), and

groups of candidates at each level are tested against the data for minimum support. (see the figure)  --

Association Rule Mining Apriori Algorithm

Figure 4.14 A Graphical Illustration of the Steps in the k-Means Algorithm.

Data Mining Software Tools

Figure 4.15 Popular Data Mining Software Tools (Poll Results).

Commercial

I B M S P S S Modeler (formerly Clementine)

S A S Enterprise Miner

Statistica - Dell/Statsoft

… many more

Free and/or Open Source

K N I M E

RapidMiner

Weka

R, …

Source: Used with permission from KDnuggets.com.

Application Case 4.6 (1 of 4)

Data Mining Goes to Hollywood: Predicting Financial Success of Movies

Goal: Predicting financial success of Hollywood movies before the start of their production process

How: Use of advanced predictive analytics methods.

Results: promising.

Application Case 4.6 (2 of 4)

Data Mining Goes to Hollywood: Predicting Financial Success of Movies

A Typical Classification Problem

Table 4.3 Movie Classification based on Receipts

Class No.	1	2	3	4	5	6	7	8	9
Range (in millions of dollars)	>1 (Flop)	>1 <610	>10 <20	>20 <640	>40 <665	>65 <6100	>100 <6150	>150 <6200	>200 (Blockbuster)

Table 4.4 Summary of Independent Variables

Independent Variable	Number of Values	Possible Values
M P A A Rating	5	G, P G, P G-13, R, N R
Competition	3	High, medium, low
Star value	3	High, medium, low
Genre	10	Sci-Fi, Historic Epic Drama, Modern Drama, Politically Related, Thriller, Horror, Comedy, Cartoon, Action, Documentary
Special effects	3	High, medium, low
Sequel	2	Yes, no
Number of screens	1	A positive integer between 1 and 3,876

Application Case 4.6 (3 of 4)

Data Mining Goes to Hollywood: Predicting Financial Success of Movies

FIGURE 4.16 Process Flow Screenshot for the Box-Office Prediction System.

The D M Process Map in I B M S P S S Modeler

Application Case 4.6 (4 of 4)

Data Mining Goes to Hollywood: Predicting Financial Success of Movies

Data Mining Myths

Table 4.6 Data Mining Myths.

Myth	Reality
Data mining provides instant, crystal-ball-like predictions.	Data mining is a multistep process that requires deliberate, proactive design and use.
Data mining is not yet viable for mainstream business applications.	The current state of the art is ready for almost any business type and/or size.
Data mining requires a separate, dedicated database.	Because of the advances in database technology, a dedicated database is not required.
Only those with advanced degrees can do data mining.	Newer Web-based tools enable managers of all educational levels to do data mining.
Data mining is only for large firms that have lots of customer data.	If the data accurately reflect the business or its customers, any company can use data mining.

Data Mining Mistakes

Selecting the wrong problem for data mining

Ignoring what your sponsor thinks data mining is and what it really can/cannot do

Beginning without the end in mind.

Not leaving insufficient time for data acquisition, selection and preparation

Looking only at aggregated results and not at individual records/predictions

… 10 more mistakes… in your book

Application Case 4.7

Predicting Customer Buying Patterns – The Target Story

Questions for Discussion:

What do you think about data mining and its implication for privacy? What is the threshold between discovery of knowledge and infringement of privacy?

Did Target go too far? Did it do anything illegal? What do you think Target should have done? What do you think Target should do next (quit these types of practices)?

End of Chapter 4

Questions / Comments

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