Chapter 4: Data Mining Business Intelligence: A Managerial Perspective on Analytics (3 rd Edition)

Chapter 4:Chapter 4:

Data Mining

Business Intelligence: Business Intelligence: A Managerial Perspective on A Managerial Perspective on

Analytics (3Analytics (3rdrd Edition) Edition)

Copyright © 2014 Pearson Education, Inc. Copyright © 2014 Pearson Education, Inc. Slide 4- Slide 4- 22

Learning ObjectivesLearning 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…)(Continued…)


Learning ObjectivesLearning 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


Opening Vignette…Opening Vignette…

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

Decision situation Problem Proposed solution Results Answer & discuss the case questions.


Questions for the Opening VignetteQuestions for the Opening Vignette1. Why should retailers, especially omni-channel retailers,

pay extra attention to advanced analytics and data mining?

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

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

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

5. What type of analytics help did Cabela’s get from their efforts? Can you think of any other potential benefits of analytics for large-scale retailers like Cabela’s?


Data Mining Concepts and Data Mining Concepts and DefinitionsDefinitionsWhy Data Mining?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 MiningDefinition 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,…


Management Science & Information Systems

Databases

Pattern Recognition

MachineLearning

MathematicalModeling

DATAMINING

Data Mining at the Intersection of Data Mining at the Intersection of Many DisciplinesMany Disciplines


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 Data Mining Characteristics/ObjectivesCharacteristics/Objectives


Application Case 4.1Application Case 4.1Smarter Insurance: Infinity P&C Improves Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Customer Service and Combats Fraud with Predictive AnalyticsPredictive AnalyticsQuestions for DiscussionQuestions 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?


Data in Data MiningData 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).- DM with

different data types?

- Other data types?

Data

Categorical Numerical

Nominal Ordinal Interval Ratio

StructuredUnstructured or Semi-Structured

MultimediaTextual HTML/XML


What Does DM Do? What Does DM Do? How Does it Work?How Does it Work? DM extracts 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 4.2Application Case 4.2Harnessing Analytics to Combat Crime: Harnessing Analytics to Combat Crime: Predictive Analytics Helps Memphis Predictive Analytics Helps Memphis Police Department Pinpoint Crime and Police Department Pinpoint Crime and Focus Police ResourcesFocus Police ResourcesQuestions for DiscussionQuestions for Discussion1.How did the Memphis Police Department use data mining to better combat crime?2.What were the challenges, the proposed solution, and the obtained results?


A Taxonomy for Data Mining TasksA Taxonomy for Data Mining TasksData Mining

Prediction

Classification

Regression

Clustering

Association

Link analysis

Sequence analysis

Learning Method Popular Algorithms

Supervised

Supervised

Supervised

Unsupervised

Unsupervised

Unsupervised

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 analysis Unsupervised K-means, Expectation Maximization (EM)

Apriory, OneR, ZeroR, Eclat

Classification and Regression Trees, ANN, SVM, Genetic Algorithms


Data Mining Tasks Data Mining Tasks 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


Data Mining ApplicationsData 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


Data Mining Applications Data Mining Applications 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 Data Mining Applications 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 Data Mining Applications Computer hardware and software Science and engineering Government and defense Homeland security and law enforcement Travel industry Healthcare Medicine Entertainment industry Sports Etc.

Highly popular application areas for data mining


Application Case 4.3Application Case 4.3A Mine on Terrorist FundingA Mine on Terrorist Funding

Questions for DiscussionQuestions for Discussion1.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.2.Do you think data mining, while essential for fighting terrorist cells, also jeopardizes individuals’ rights of privacy?


Data Mining ProcessData Mining Process A manifestation of best practices A systematic way to conduct DM projects Different groups have 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)


Data Mining ProcessData Mining Process

Source: KDNuggets.com


Data Mining Process: CRISP-DMData Mining Process: CRISP-DM

Data Sources

Business Understanding

Data Preparation

Model Building

Testing and Evaluation

Deployment

Data Understanding

6

1 2

3

5

4


Data Mining Process: CRISP-DMData Mining Process: CRISP-DMStep 1: Business UnderstandingStep 2: Data UnderstandingStep 3: Data Preparation (!)Step 4: Model BuildingStep 5: Testing and EvaluationStep 6: Deployment The process is highly repetitive and

experimental (DM: art versus science?)

Accounts for ~85% of total project time


Data Preparation – A Critical DM Data Preparation – A Critical DM TaskTask

Data Consolidation

Data Cleaning

Data Transformation

Data Reduction

Well-formedData

Real-worldData

· 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


Data Mining Process: SEMMAData Mining Process: SEMMA

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


Application Case 4.4Application Case 4.4

Data Mining in Cancer ResearchData Mining in Cancer Research

Questions for DiscussionQuestions for Discussion1.How can data mining be used for ultimately curing illnesses like cancer?

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


Data Mining Methods: Data Mining Methods: ClassificationClassification 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?


Predictive accuracy Hit rate

Speed Model building; predicting

Robustness Scalability Interpretability Transparency, explainability

Assessment Methods for Assessment Methods for ClassificationClassification


Accuracy of Classification ModelsAccuracy of Classification Models In classification problems, the primary source for

accuracy estimation is the confusion matrix

True Positive

Count (TP)

FalsePositive

Count (FP)

TrueNegative

Count (TN)

FalseNegative

Count (FN)

True Class

Positive Negative

Pos

itive

Neg

ativ

e

Pre

dict

ed C

lass FNTP

TPRatePositiveTrue

FPTN

TNRateNegativeTrue

FNFPTNTP

TNTPAccuracy

FPTP

TPrecision

P

FNTP

TPcallRe


Estimation Methodologies for Estimation Methodologies for ClassificationClassification 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%])

PreprocessedData

Training Data

Testing Data

Model Development

Model Assessment

(scoring)

2/3

1/3

Classifier

Prediction Accuracy


Estimation Methodologies for Estimation Methodologies for ClassificationClassification 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


Estimation Methodologies for Estimation Methodologies for Classification – ROC CurveClassification – ROC Curve

10.90.80.70.60.50.40.30.20.10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1

0.9

0.8

False Positive Rate (1 - Specificity)

Tru

e P

ositi

ve R

ate

(Sen

sitiv

ity) A

B

C


Classification TechniquesClassification Techniques Decision tree analysis Statistical analysis Neural networks Support vector machines Case-based reasoning Bayesian classifiers Genetic algorithms Rough sets


Decision TreesDecision Trees

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

2. Select the best splitting attribute.

3. 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.

4. Repeat 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


Decision Trees Decision Trees DT algorithms mainly differ on

1. Splitting criteria Which variable, what value, etc.

2. Stopping criteria When to stop building the tree

3. Pruning (generalization method) Pre-pruning versus post-pruning

Most popular DT algorithms include ID3, C4.5, C5; CART; CHAID; M5


Decision TreesDecision 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)


Application Case 4.5Application Case 4.52degrees Gets a 1275 Percent Boost in 2degrees Gets a 1275 Percent Boost in Churn IdentificationChurn IdentificationQuestions for DiscussionQuestions for Discussion1.What does 2degrees do? Why is it important for 2degrees to accurately identify churn?2.What were the challenges, the proposed solution, and the obtained results?3.How can data mining help in identifying customer churn? How do some companies do it without using data mining tools and techniques?4.Why is it important for Delta Lloyd Group to comply with industry regulations?


Cluster Analysis for Data MiningCluster Analysis for Data Mining Used for automatic identification of

natural groupings of things Part of the machine-learning family Employs unsupervised learning Learns the clusters of things from past

data, then assigns new instances There is not an output variable Also known as segmentation


Cluster Analysis for Data MiningCluster 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)


Cluster Analysis for Data MiningCluster 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


Cluster Analysis for Data MiningCluster 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


Cluster Analysis for Data MiningCluster 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).


Cluster Analysis for Data Mining - Cluster Analysis for Data Mining - kk-Means Clustering Algorithm-Means Clustering Algorithm

Step 1 Step 2 Step 3


Association Rule MiningAssociation 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!”


Association Rule MiningAssociation 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!


Association Rule MiningAssociation Rule Mining A representative application of association rule

mining includes 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)

…


Association Rule MiningAssociation 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 goes together with X

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


Association Rule MiningAssociation 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


Association Rule MiningAssociation 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) --


Association Rule MiningAssociation Rule MiningApriori AlgorithmApriori Algorithm

Itemset(SKUs)

SupportTransaction

NoSKUs

(Item No)

1

1

1

1

1

1

1, 2, 3, 4

2, 3, 4

2, 3

1, 2, 4

1, 2, 3, 4

2, 4

Raw Transaction Data

1

2

3

4

3

6

4

5

Itemset(SKUs)

Support

1, 2

1, 3

1, 4

2, 3

3

2

3

4

3, 4

5

3

2, 4

Itemset(SKUs)

Support

1, 2, 4

2, 3, 4

3

3

One-item Itemsets Two-item Itemsets Three-item Itemsets


Artificial Neural Networks Artificial Neural Networks for Data Miningfor Data Mining Artificial neural networks (ANN or NN) are a brain

metaphor for information processing a.k.a. Neural Computing Very good at capturing highly complex non-linear

functions! Many uses – prediction (regression, classification),

clustering/segmentation Many application areas - finance, medicine, marketing,

manufacturing, service operations, information systems, …

Biological Biological versus versus Artificial Artificial Neural Neural NetworksNetworks

Neuron

Axon

Axon

SynapseSynapse

Dendrites

Dendrites Neuron

w1

w2

wn

x1

x2

xn

.

.

.

Y

Y1

Yn

Y2

Inputs

Weights

Outputs

.

.

.

Processing Element (PE)

n

iiiWXS

1

)( Sf

Summation

TransferFunction

Biological Artificial

Neuron

Dendrites

Axon

Synapse

Slow

Many (109)

Node (or PE)

Input

Output

Weight

Fast

Few (102)

Biological NN

Artificial NN


Elements/Concepts of ANNElements/Concepts of ANN Processing element (PE) Information processing Network structure Feedforward vs. recurrent vs. multi-layer…

Learning parameters Supervised/unsupervised, backpropagation,

learning rate, momentum ANN Software – NN shells, integrated modules

in comprehensive DM software, …


Data Mining Data Mining SoftwareSoftware

Commercial IBM SPSS Modeler

(formerly Clementine) SAS - Enterprise Miner IBM - Intelligent Miner StatSoft – Statistica Data

Miner … many more

Free and/or Open Source R RapidMiner Weka…

0 50 100 150 200 250 300

Predixion 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)

Source: KDNuggets.com


Big Data Software Tools and Big Data Software Tools and PlatformsPlatforms

0 10 20 30 40 50 60 70 80

Other Hadoop-based tools (10)

Other Big Data software (21)

NoSQL databases (33)

Amazon Web Services (AWS) (36)

Apache Hadoop/Hbase/Pig/Hive (67)

0 50 100 150 200 250 300

F# (5)

Awk/Gawk/Shell (31)

Perl (37)

Other languages (57)

C/C++ (66)

Python (119)

Java (138)

SQL (185)

R (245)


Application Case 4.6Application Case 4.6

Data Mining Goes to Hollywood: Data Mining Goes to Hollywood: Predicting Financial Success of MoviesPredicting Financial Success of Movies

Questions for DiscussionQuestions for Discussion Decision situation Problem Proposed solution Results Answer & discuss the case questions.


Application Case 4.6Application Case 4.6Data Mining Goes to Hollywood!Data Mining Goes to Hollywood!

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)

DependeDepende

nt nt VariableVariable IndependeIndepende

nt nt VariablesVariables

A Typical A Typical Classification Classification

ProblemProblem



ModelDevelopmentprocess

ModelAssessmentprocess

The DM The DM Process Process Map in Map in

IBM IBM SPSS SPSS

ModeleModelerr



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


Data Mining MythsData 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


Common Data Mining BlundersCommon Data Mining Blunders1. Selecting the wrong problem for data mining

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

3. Not leaving sufficient time for data acquisition, selection, and preparation

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

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

6. …more in the book


Application Case 4.7Application Case 4.7Data Mining & PrivacyData Mining & PrivacyPredicting Customer Buying Patterns—Predicting Customer Buying Patterns—The Target StoryThe Target Story

Questions for DiscussionQuestions 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 the ChapterEnd of the Chapter

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Chapter 4: Data Mining Business Intelligence: A Managerial Perspective on Analytics (3 rd Edition)