Business Intelligence week 3

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Chapter 5:

Data Mining

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

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Learning Objectives

Define data mining as an enabling technology for business intelligence

Understand the objectives and benefits of business analytics and data mining

Recognize the wide range of applications of data mining

Learn the standardized data mining processes

CRISP-DM

SEMMA

KDD

(Continued…)

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Learning Objectives

Understand the steps involved in data preprocessing for data mining

Learn different methods and algorithms of data mining

Build awareness of the existing data mining software tools

Commercial versus free/open source

Understand the pitfalls and myths of data mining

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Opening Vignette…

Cabela’s Reels in More Customers with Advanced Analytics and Data Mining

Decision situation

Problem

Proposed solution

Results

Answer & discuss the case questions.

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Questions for the Opening Vignette

Why should retailers, especially omni-channel retailers, pay extra attention to advanced analytics and data mining?

What are the top challenges for multi-channel retailers? Can you think of other industry segments that face similar problems/challenges?

What are the sources of data that retailers such as Cabela’s use for their data mining projects?

What does it mean to have a “single view of the customer”? How can it be accomplished?

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Data Mining Concepts/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.

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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,…

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Data Mining is at the Intersection of Many Disciplines

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

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

Data is the most critical ingredient for DM 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.)

Data Mining Characteristics/Objectives

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Application Case 5.1

Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Predictive Analytics

Questions For Discussion

How did Infinity P&C improve customer service with data mining?

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

What was their implementation strategy? Why is it important to produce results as early as possible in data mining studies?

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Data in Data Mining

Data: a collection of facts usually obtained as the result of experiences, observations, or experiments.

Data may consist of numbers, words, images, …

Data: lowest level of abstraction (from which information and knowledge are derived).

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DM 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

What Does DM Do? How Does it Work?

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Application Case 5.2

Harnessing Analytics to Combat Crime: Predictive Analytics Helps Memphis Police Department Pinpoint Crime and Focus Police Resources

Questions For Discussion

How did the Memphis Police Department used data mining to better combat crime?

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

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A Taxonomy for Data Mining Tasks

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Data Mining Tasks (cont.)

Time-series forecasting

Part of sequence or link analysis?

Visualization

Another data mining task?

Types of DM

Hypothesis-driven data mining

Discovery-driven data mining

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Data Mining Applications

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

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Data Mining Applications (cont.)

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

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Data Mining Applications (cont.)

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

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Data Mining Applications (cont.)

Computer hardware and software

Science and engineering

Government and defense

Homeland security and law enforcement

Travel industry

Healthcare

Medicine

Entertainment industry

Sports

Etc.

Increasingly more popular application areas for data mining

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Application Case 5.3

A Mine on 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, while essential for fighting terrorist cells, also jeopardizes individuals’ rights of privacy?

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Data Mining Process

A manifestation of best practices

A systematic way to conduct DM projects

Different groups has different versions

Most common standard processes:

CRISP-DM (Cross-Industry Standard Process for Data Mining)

SEMMA (Sample, Explore, Modify, Model, and Assess)

KDD (Knowledge Discovery in Databases)

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Data Mining Process

Source: KDNuggets.com

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Data Mining Process: CRISP-DM

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Data Mining Process: CRISP-DM

Step 1: Business Understanding

Step 2: Data Understanding

Step 3: Data Preparation (!)

Step 4: Model Building

Step 5: Testing and Evaluation

Step 6: Deployment

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

Accounts for ~85% of total project time

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Data Preparation – A Critical DM Task

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Data Mining Process: SEMMA

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Application Case 5.4

Data Mining 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?

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Data Mining Methods: Classification

Most frequently used DM 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?

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Predictive accuracy

Hit rate

Speed

Model building; predicting

Robustness

Scalability

Interpretability

Transparency, explainability

Assessment Methods for Classification

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Accuracy of Classification Models

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

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Estimation Methodologies for Classification

Simple split (or holdout or test sample estimation)

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

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

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Estimation Methodologies for Classification

k-Fold Cross Validation (rotation estimation)

Split the data into k mutually exclusive subsets

Use each subset as testing while using the rest of the subsets as training

Repeat the experimentation for k times

Aggregate the test results for true estimation of prediction accuracy training

Other estimation methodologies

Leave-one-out, bootstrapping, jackknifing

Area under the ROC curve

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Estimation Methodologies for Classification – ROC Curve

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Classification Techniques

Decision tree analysis

Statistical analysis

Neural networks

Support vector machines

Case-based reasoning

Bayesian classifiers

Genetic algorithms

Rough sets

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Decision Trees

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.

A general algorithm for decision tree building

Employs the divide and conquer method

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

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Decision Trees

DT 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 DT algorithms include

ID3, C4.5, C5; CART; CHAID; M5

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Decision Trees

Alternative splitting criteria

Gini index determines the purity of a specific class as a result of a decision to branch along a particular attribute/value

Used in CART

Information gain uses entropy to measure the extent of uncertainty or randomness of a particular attribute/value split

Used in ID3, C4.5, C5

Chi-square statistics (used in CHAID)

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Application Case 5.5

2degrees Gets a 1275 Percent Boost in Churn Identification

Questions For Discussion

What does 2degrees do? Why is it important for 2degrees to accurately identify churn?

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

How can data mining help in identifying customer churn? How do some companies do it without using data mining tools and techniques?

Why is it important for Delta Lloyd Group to comply with industry regulations?

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Cluster Analysis for Data Mining

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 variable

Also known as segmentation

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Cluster Analysis for Data Mining

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)

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Cluster Analysis for Data Mining

Analysis methods

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

Neural networks (adaptive resonance theory [ART], self-organizing map [SOM])

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

Genetic algorithms

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Cluster Analysis for Data Mining

How many clusters?

There is not a “truly optimal” way to calculate it

Heuristics are often used

Most cluster analysis methods involve the use of a distance measure to calculate the closeness between pairs of items.

Euclidian versus Manhattan/Rectilinear distance

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Cluster Analysis for Data Mining

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).

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Cluster Analysis for Data Mining - k-Means Clustering Algorithm

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Association Rule Mining

A very popular DM 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 DM to ordinary people, such as the famous “relationship between diapers and beers!”

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Association Rule Mining

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!

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Association Rule Mining

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 DSS); and genes and their functions (to be used in genomics projects)

…

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Association Rule Mining

Are all association rules interesting and useful?

A Generic Rule: X  Y [S%, C%]

X, Y: products and/or services

X: Left-hand-side (LHS)

Y: Right-hand-side (RHS)

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%]

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Association Rule Mining

Algorithms are available for generating association rules

Apriori

Eclat

FP-Growth

+ Derivatives and hybrids of the three

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

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Association Rule Mining

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)  --

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Association Rule Mining Apriori Algorithm

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Data Mining Software

Commercial

IBM SPSS Modeler (formerly Clementine)

SAS - Enterprise Miner

IBM - Intelligent Miner

StatSoft – Statistica Data Miner

… many more

Free and/or Open Source

RapidMiner

Weka…

Source: KDNuggets.com

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Big Data Software Tools and Platforms

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Application Case 5.6

Data Mining Goes to Hollywood: Predicting Financial Success of Movies

Questions For Discussion

Decision situation

Problem

Proposed solution

Results

Answer & discuss the case questions.

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Application Case 5.6 Data Mining Goes to Hollywood!

Dependent Variable

Independent Variables

A Typical Classification Problem

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Application Case 5.6 Data Mining Goes to Hollywood!

The DM Process Map in IBM SPSS Modeler

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Application Case 5.6 Data Mining Goes to Hollywood!

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Application Case 5.7 Data Mining & Privacy Issues

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)?

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Data Mining Myths

Data mining …

provides instant solutions/predictions

is not yet viable for business applications

requires a separate, dedicated database

can only be done by those with advanced degrees

is only for large firms that have lots of customer data

is another name for the good-old statistics

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Common Data Mining Blunders

Selecting the wrong problem for data mining

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

Not leaving insufficient time for data acquisition, selection and preparation

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

Being sloppy about keeping track of the data mining procedure and results

…more in the book

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End of the Chapter

Questions, comments

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

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Management Science &

Information Systems

Databases

Pattern

Recognition

Machine

Learning

Mathematical

Modeling

DATA

MINING

Data

CategoricalNumerical

NominalOrdinalIntervalRatio

Structured

Unstructured or

Semi-Structured

MultimediaTextualHTML/XML

Data Mining

Prediction

Classification

Regression

Clustering

Association

Link analysis

Sequence analysis

Learning MethodPopular Algorithms

Supervised

Unsupervised

Decision trees, ANN/MLP, SVM, Rough

sets, Genetic Algorithms

Linear/Nonlinear Regression, Regression

trees, ANN/MLP, SVM

Expectation Maximization, Apriory

Algorithm, Graph-based Matching

Apriory Algorithm, FP-Growth technique

K-means, ANN/SOM

Outlier analysisUnsupervisedK-means, Expectation Maximization (EM)

Apriory, OneR, ZeroR, Eclat

Classification and Regression Trees,

ANN, SVM, Genetic Algorithms

Data Sources

Business

Understanding

Data

Preparation

Model

Building

Testing and

Evaluation

Deployment

Data

Understanding

Data Consolidation

Data Cleaning

Data Transformation

Data Reduction

Well-formed

Data

Real-world

Data

·Collect data

·Select data

·Integrate data

·Impute missing values

·Reduce noise in data

·Eliminate inconsistencies

·Normalize data

·Discretize/aggregate data

·Construct new attributes

·Reduce number of variables

·Reduce number of cases

·Balance skewed data

Sample

(Generate a representative

sample of the data)

Modify

(Select variables, transform

variable representations)

Explore

(Visualization and basic

description of the data)

Model

(Use variety of statistical and

machine learning models )

Assess

(Evaluate the accuracy and

usefulness of the models)

SEMMA

Rate

Positive

True

Rate

Negative

True

Accuracy

recision

call

True

Positive

Count (TP)

False

Positive

Count (FP)

True

Negative

Count (TN)

False

Negative

Count (FN)

True Class

PositiveNegative

Preprocessed

Data

Training Data

Testing Data

Model

Development

Model

Assessment

(scoring)

2/3

1/3

Classifier

Prediction

Accuracy

10.90.80.70.60.50.40.30.20.10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.9

0.8

False Positive Rate (1 - Specificity)

(

)

Step 1Step 2Step 3

Itemset

(SKUs)

Support

Transaction

SKUs

(Item No)

1, 2, 3, 4

2, 3, 4

2, 3

1, 2, 4

1, 2, 3, 4

2, 4

Raw Transaction Data

Itemset

(SKUs)

Support

1, 2

1, 3

1, 4

2, 3

3, 4

2, 4

Itemset

(SKUs)

Support

1, 2, 4

2, 3, 4

One-item ItemsetsTwo-item ItemsetsThree-item Itemsets

050100150200250300Predixion Software (3)WordStat (3)11 Ants Analytics (4)Teradata Miner (4)RapidInsight/Veera (5)Angoss (7)SAP (BusinessObjects/Sybase/Hana)(7)XLSTAT (7)Salford SPM/CART/MARS/TreeNet/RF (9)Revolution Computing (11)C4.5/C5.0/See5 (13)Bayesia (14)KXEN (14)Zementis (14)Stata (15)IBM Cognos (16)Miner3D (19)Mathematica (23)JMP (32)Other commercial software (32)Oracle Data Miner (35)Tableau (35)TIBCO Spotfire / S+ / Miner (37)Other free software (39)Microsoft SQL Server (40)Orange (42)SAS Enterprise Miner (46)IBM SPSS Modeler (54)IBM SPSS Statistics (62)MATLAB (80)Rapid-I RapidAnalytics (83)SAS (101)StatSoft Statistica (112)Weka / Pentaho (118)KNIME (174)Rapid-I RapidMiner (213)Excel (238)R (245)

01020304050607080Other Hadoop-based tools (10)Other Big Data software (21)NoSQL databases (33)Amazon Web Services (AWS) (36)Apache Hadoop/Hbase/Pig/Hive (67)

050100150200250300F# (5)Awk/Gawk/Shell (31)Perl (37)Other languages (57)C/C++ (66)Python (119)Java (138)SQL (185)R (245)

Independent Variable

Number of

Values

Possible Values

MPAA Rating 5 G, PG, PG -13, R, NR

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 1 Yes, No

Number of screens 1 Positive integer

Class No. 1 2 3 4 5 6 7 8 9

Range

(in $Millions)

< 1

(Flop)

> 1

< 10

> 10

< 20

> 20

< 40

> 40

< 65

> 65

< 100

> 100

< 150

> 150

< 200

> 200

(Blockbuster)

Model

Development

process

Model

Assessment

process

Prediction Models

Individual Models Ensemble Models

Performance

Measure SVM ANN C&RT

Random

Forest

Boosted

Tree

Fusion

(Average)

Count (Bingo)

192 182 140 189 187

194

Count (1-Away)

104 120 126 121 104

120

Accuracy (% Bingo)

55.49% 52.60% 40.46% 54.62% 54.05%

56.07%

Accuracy (% 1-Away)

85.55% 87.28% 76.88% 89.60% 84.10%

90.75%

Standard deviation

0.93 0.87 1.05 0.76 0.84

0.63

* Training set: 1998 – 2005 movies; Test set: 2006 movies