zhangxi lin isqs 3358 texas tech university 1. define data mining and list its objectives and...

Zhangxi LinISQS 3358Texas Tech University

1

Define data mining and list its objectives and benefits

Understand different purposes and applications of data mining

Understand different methods of data mining, especially clustering and decision tree models

Build expertise in use of some data mining software

Learn the process of data mining projects Understand data mining pitfalls and

myths Define text mining and its objectives and

benefits Appreciate use of text mining in business

applications Define Web mining and its objectives and

benefits

ISQS 6347, Data & Text Mining 4

Case 1: Credit Card Promotion

Credit card companies periodically send promotion offers, e.g. life insurance promotion, to some potential customers. Assume:

Each promotion letter costs $0.20 The profit from each promotion acceptance is $10 Overall response rate is 1%

Question: Sending the offer to unselected population will result

in the expected average profit $10 * 1% - $0.2 * 99% = -$0.098 ---- a loss. How to send the promotion offers to the right customers in order to make profit?

How to maximize the profit by applying a proper set of selection rules?

Case 2: Customer Segmentation

ID Name Gender Age Occupation C001 X M 15 StudentC002 Y F 30 StaffC003 Z M 18 StudentC004 A F 45 StaffC005 B M 30 StaffC006 C F 25 Student

The data is used to segment the customers for sell promotionThree products: DVD, game, a drink for adultProblems

How to segment the customers into two clustersIs two clusters good enough? Why not three clusters

Data & Text Mining 5

Data & Text Mining 6

Case 3: Association Rule Mining Given a set of transactions, find rules that will

predict the occurrence of an item based on the occurrences of other items in the transaction

Market-Basket transactions

TID Items

1 Bread, Milk

2 Bread, Diaper, Beer, Eggs

3 Milk, Diaper, Beer, Coke

4 Bread, Milk, Diaper, Beer

5 Bread, Milk, Diaper, Coke

Example of Association Rules

{Diaper} {Beer},{Milk, Bread} {Eggs,Coke},{Beer, Bread} {Milk},

Implication means co-occurrence, not causality!

Data mining (DM) A process that uses statistical, mathematical, artificial intelligence and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases

Knowledge discovery in databases (KDD)

A comprehensive process of using data mining methods to find useful information and patterns in data

Major characteristics and objectives of data mining Data are often buried deep within very

large databases, which sometimes contain data from several years; sometimes the data are cleansed and consolidated in a data warehouse

The data mining environment is usually client/server architecture or a Web-based architecture

Major characteristics and objectives of data mining Sophisticated new tools help to remove the

information ore buried in corporate files or archival public records; finding it involves massaging and synchronizing the data to get the right results.

The miner is often an end user, empowered by data drills and other power query tools to ask ad hoc questions and obtain answers quickly, with little or no programming skill

Major characteristics and objectives of data mining Striking it rich often involves finding an

unexpected result and requires end users to think creatively

Data mining tools are readily combined with spreadsheets and other software development tools; the mined data can be analyzed and processed quickly and easily

Parallel processing is sometimes used because of the large amounts of data and massive search efforts

How data mining works Data mining tools find patterns in data

and may even infer rules from them Three methods are used to identify

patterns in data:1. Simple models 2. Intermediate models 3. Complex models

Classification Supervised induction used to analyze the historical data stored in a database and to automatically generate a model that can predict future behavior

Common tools used for classification are: Neural networks Decision trees If-then-else rules

Clustering Partitioning a database into segments in which the members of a segment share similar qualities

Association A category of data mining algorithm that establishes relationships about items that occur together in a given record

Sequence discovery The identification of associations over time

Visualization can be used in conjunction with data mining to gain a clearer understanding of many underlying relationships

Regression is a well-known statistical technique that is used to map data to a prediction value

Forecasting estimates future values based on patterns within large sets of data

– Marketing– Banking– Retailing and sales– Manufacturing and

production– Brokerage and

securities trading– Insurance

– Computer hardware and software

– Government and defense

– Airlines– Health care– Broadcasting – Police– Homeland security

Data mining applications


20%

80%

Data mining tools and techniques can be classified based on the structure of the data and the algorithms used:

Statistical methods Decision trees

Defined as a root followed by internal nodes. Each node (including root) is labeled with a question and arcs associated with each node cover all possible responses

Data mining tools and techniques can be classified based on the structure of the data and the algorithms used:

Case-based reasoning Neural computing Intelligent agents Genetic algorithms Other tools

Rule induction Data visualization

A general algorithm for building a decision tree:

1. Create a root node and select a splitting attribute.

2. Add a branch to the root node for each split candidate value and label

3. Take the following iterative steps:a. Classify data by applying the split value.b. If a stopping point is reached, then create

leaf node and label it. Otherwise, build another subtree

Gini index Used in economics to measure the diversity of the population. The same concept can be used to determine the ‘purity’ of a specific class as a result of a decision to branch along a particular attribute/variable

ISQS 6347, Data & Text Mining

23

Gini Index for a given node t :

(NOTE: p( j | t) is the relative frequency of class j at node t).

Maximum (1 - 1/nc) when records are equally distributed among all classes, implying least interesting information

Minimum (0.0) when all records belong to one class, implying most interesting information

j

tjptGINI 2)]|([1)(

C1 0C2 6

Gini=0.000

C1 2C2 4

Gini=0.444

C1 3C2 3

Gini=0.500

C1 1C2 5

Gini=0.278


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C1 0 C2 6

C1 2 C2 4

C1 1 C2 5

P(C1) = 0/6 = 0 P(C2) = 6/6 = 1

Gini = 1 – P(C1)2 – P(C2)2 = 1 – 0 – 1 = 0

j

tjptGINI 2)]|([1)(

P(C1) = 1/6 P(C2) = 5/6

Gini = 1 – (1/6)2 – (5/6)2 = 0.278

P(C1) = 2/6 P(C2) = 4/6

Gini = 1 – (2/6)2 – (4/6)2 = 0.444

The ID3 (Iterative Dichotomizer 3) algorithm decision tree approach

Entropy Measures the extent of uncertainty or randomness in a data set. If all the data in a subset belong to just one class, then there is no uncertainty or randomness in that dataset, therefore the entropy is zero


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Collection of data objects and their attributes (variables)

An attribute is a property or characteristic of an object

Examples: eye color of a person, temperature, etc.

Attribute is also known as variable, field, characteristic, or feature

A collection of attributes describe an object

Object is also known as record, point, case, sample, entity, or instance

Tid Refund Marital Status

Taxable Income Cheat

1 Yes Single 125K No

2 No Married 100K No

3 No Single 70K No

4 Yes Married 120K No

5 No Divorced 95K Yes

6 No Married 60K No

7 Yes Divorced 220K No

8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes 10

Attributes (Variables)

Objects


Apply

Model

Induction

Deduction

Learn

Model

Model

Tid Attrib1 Attrib2 Attrib3 Class

1 Yes Large 125K No

2 No Medium 100K No

3 No Small 70K No

4 Yes Medium 120K No

5 No Large 95K Yes

6 No Medium 60K No

7 Yes Large 220K No

8 No Small 85K Yes

9 No Medium 75K No

10 No Small 90K Yes 10


11 No Small 55K ?

12 Yes Medium 80K ?

13 Yes Large 110K ?

14 No Small 95K ?

15 No Large 67K ? 10

Test Set

Learningalgorithm

Training Set


Tid Refund MaritalStatus

TaxableIncome Cheat



3 No Single 70K No



6 No Married 60K No


8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes10

categoric

al

categoric

al

continuous

class

Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No

Married Single, Divorced

< 80K > 80K

Splitting Attributes

Training Data Model: Decision Tree


Tid Refund MaritalStatus

TaxableIncome Cheat



3 No Single 70K No



6 No Married 60K No


8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes10

categoric

al

categoric

al

continuous

classMarSt

Refund

TaxInc

YESNO

NO

NO

Yes No

Married Single,

Divorced

< 80K > 80K

There could be more than one tree that fits the same data!


Apply

Model

Induction

Deduction

Learn

Model

Model


1 Yes Large 125K No

2 No Medium 100K No

3 No Small 70K No


5 No Large 95K Yes

6 No Medium 60K No

7 Yes Large 220K No

8 No Small 85K Yes

9 No Medium 75K No



11 No Small 55K ?

12 Yes Medium 80K ?

13 Yes Large 110K ?

14 No Small 95K ?


Test Set

TreeInductionalgorithm

Training SetDecision Tree


Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K

Refund Marital Status


No Married 80K ? 10

Test DataStart from the root of tree.


Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K



No Married 80K ? 10

Test Data


Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K



No Married 80K ? 10

Test Data

Assign Cheat to “No”


Apply

Model

Induction

Deduction

Learn

Model

Model


1 Yes Large 125K No

2 No Medium 100K No

3 No Small 70K No


5 No Large 95K Yes

6 No Medium 60K No

7 Yes Large 220K No

8 No Small 85K Yes

9 No Medium 75K No



11 No Small 55K ?

12 Yes Medium 80K ?

13 Yes Large 110K ?

14 No Small 95K ?


Test Set

TreeInductionalgorithm

Training Set

Decision Tree


ActualAccept

ActualReject

Computed Accept

Computed Reject

True Positive (TP)a

True Negative (TN)d

False Positive (FP)c

False Negative (FN)b

Accuracy rate = a / (a + c), Coverage rate = a / (a + b)Lift = Accuracy rate / [(a + b) / (a + b + c + d)]

a + b

c + d

a + c b + d

Cluster analysis for data mining Cluster analysis is an exploratory data

analysis tool for solving classification problems

The object is to sort cases into groups so that the degree of association is strong between members of the same cluster and weak between members of different clusters

Cluster analysis results may be used to:

Help identify a classification scheme Suggest statistical models to describe

populations Indicate rules for assigning new cases to

classes for identification, targeting, and diagnostic purposes

Provide measures of definition, size, and change in what were previously broad concepts

Find typical cases to represent classes

Cluster analysis methods Statistical methods Optimal methods Neural networks Fuzzy logic Genetic algorithms

Each of these methods generally works with one of two general method classes:

Divisive Agglomerative

Hierarchical clustering method and example

1. Decide which data to record from the items 2. Calculate the distances between all initial

clusters. Store the results in a distance matrix3. Search through the distance matrix and find the

two most similar clusters4. Fuse those two clusters together to produce a

cluster that has at least two items5. Calculate the distances between this new cluster

and all the other clusters6. Repeat steps 3 to 5 until you have reached the

prespecified maximum number of clusters

Classes of data mining tools and techniques as they relate to information and business intelligence (BI) technologies

Mathematical and statistical analysis packages Personalization tools for Web-based marketing Analytics built into marketing platforms Advanced CRM tools Analytics added to other vertical industry-

specific platforms Analytics added to database tools (e.g., OLAP) Standalone data mining tools

45

What Is Text Mining? Text mining is a process that

employs a set of algorithms for converting unstructured text into structured data objects and the quantitative methods used to analyze these data objects.

Text Mining Case: Federalist papers Alexander Hamilton, James Madison, and John Jay wrote a series of

essays in 1787 and 1788 to try to convince the citizens of the state of New York to ratify the new constitution of the United States. These essays are collectively called The Federalist Papers. Copies of the papers in a variety of formats can be found at

http://www.yale.edu/lawweb/avalon/federal/fed.htm, or http://www.constitution.org/fed/federa00.htm.

Of the 85 essays, 51 are attributed to Hamilton, 15 to Madison, 5 to Jay, and 3 to Hamilton and Madison jointly. The 11 remaining essays can be attributed only to Hamilton or Madison. Mosteller and Wallace (1964) used Bayesian statistical techniques to provide evidence that Madison wrote all 11 of the essays of unknown authorship. (The essays in question are numbers 49, 50, 51, 52, 53, 54, 55, 56, 57, 62, and 63.)

Problem: Uniquely identify an author based on the distribution of words in a document.

46

http://www.yale.edu/lawweb/avalon/federal/fed.htm

http://www.yale.edu/lawweb/avalon/federal/fed.htm

http://www.constitution.org/fed/federa00.htm

47

A simple text mining example

A tiny case - 9 documents deposit the cash and check in the bank - Fin the river boat is on the bank - Riv borrow based on credit - Fin river boat floats up the river - Riv boat is by the dock near the bank - Riv with credit, I can borrow cash from the bank - Fin boat floats by dock near the river bank - Riv check the parade route to see the floats - Par along the parade route - Par

Text mining helps organizations: Find the “hidden” content of documents,

including additional useful relationships Relate documents across previous

unnoticed divisions Group documents by common themes

Applications of text mining Automatic detection of e-mail spam or

phishing through analysis of the document content

Automatic processing of messages or e-mails to route a message to the most appropriate party to process that message

Analysis of warranty claims, help desk calls/reports, and so on to identify the most common problems and relevant responses

Applications of text mining Analysis of related scientific publications

in journals to create an automated summary view of a particular discipline

Creation of a “relationship view” of a document collection

Qualitative analysis of documents to detect deception

How to mine text 1. Eliminate commonly used words (stop-

words)2. Replace words with their stems or roots

(stemming algorithms)3. Consider synonyms and phrases 4. Calculate the weights of the remaining

terms

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Example

Coca-Cola announced earnings on Saturday, Dec. 12, 2000. Profits were up by 3.1% as of 12/12/1999.

coca-cola + announce earnings on Saturday dec. 12 2000 + profit + be up 3.1% as of 2000-12-12 1999-12-12

Web mining The discovery and analysis of interesting and useful information from the Web, about the Web, and usually through Web-based tools

Web content mining The extraction of useful information from Web pages

Web structure mining The development of useful information from the links included in the Web documents

Web usage mining The extraction of useful information from the data being generated through webpage visits, transaction, etc.

Uses for Web mining: Determine the lifetime value of clients Design cross-marketing strategies

across products Evaluate promotional campaigns Target electronic ads and coupons at

user groups Predict user behavior Present dynamic information to users

58

Banners

Landing page

Sign up

Target page

Click

Click

Click

BANNERAD

ABANDON

PROPBUY

Buy

Exit

Exit

Exit

Exit

Depth of conversion

Sign up

First time purchase

Repeated purchase

Data

Data

Data

Data

How to improve the effectiveness of banner advertising? Understand the context:

Availability of the information: click-through flow, user profile, etc.

Multiple ads – which one should be used? Data collection Data mining Model evaluation

60

Model can be built using Web log data Registration data Vendor data (may not be required)

One model with indicator for banner ad/vendor selected

Multiple models, one for each vendor Overlapping data if page sequences are included,

because “did not click” entries will have common elements in all models

Model scores the propensity to click on a vendor’s banner ad

In the case there is only one slot for one of two ads, which one is the best decision: Selectively place an ad from the two choices Randomly place one of the ads Place both with two slots, or time-sharing

alternatively Place nothing when the likelihood of the click-

through is low, because of the possible negative effect.

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SAS Enterprise Miner 4.3

Basic How to use the application main menu Using the pop-up menus Enterprise Miner documentation Project – Diagram

The SEMMA methodology Sample Explore Modify Model Assess

Decision Tree Example (pp147-151)

Income Pattern# Loan risk

17 1 High

20 5 High

23 0 High

32 4 Low

43 2 High

68 3 Low

MBA Admission Decision Problem

GMAT GPA Quantitative GAMT Score (percentile)

Decision

650 2.75 35 No

580 3.50 70 No

600 3.50 75 Yes

450 2.95 80 No

700 3.25 90 Yes

590 3.50 80 Yes

400 3.85 45 No

640 3.50 75 Yes

540 3.00 60 ?

590 2.85 80 ?

490 4.00 65 ?

zhangxi lin isqs 3358 texas tech university 1. define data mining and list its objectives and...

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