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UNIT Chapter in Book Topic Page

Number

UNIT 1

1

INTRODUCTION

What is Data Mining? 1

Motivating Challenges 2

The origins of data mining 4

Data Mining Tasks 6

2 DATA 1

Types of Data Attribute and

Measurement

19

Types of Data

Sets

23

Data Quality Measurement &

data Collection

Issues

36

Issues Related

To Application

43

UNIT 2 2 DATA 2

Data Preprocessing Aggregation 45

Sampling 47

Dimensionality

Reduction

50

Feature Subset

Selection

52

Feature

Creation

55

Discretization &

Binarization

57

Variable

Transformation

63

Measures of Similarity and Dissimilarity Basics 66

Similarity &

Dissimilarity

between Simple

Attributes

67

Dissimilarities

Between Data

Objects

69

Similarities

Between Data

Objects

72

Examples if

ProximityMeasures

73

Issues in

Proximity

Calculation

80

Selecting The

Right Proximity

Measure

83

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UNIT CHAPTER IN BOOK TOPIC CONTENT PAGE NO.

UNIT

3

4 CLASSIFICATION

Preliminaries 146

General approach to

solving a classification

problem

148

Decision tree induction How a Decision Tree Works 150

How To Build A Decision

Tree

151

Method for expressing

attribute test conditions

155

Measure for selecting the

best split

158

Algorithm for decision tree

induction

164

An example : web robot

detection

166

Characteristics Of decision

tree induction

168

5 CLASSIFICATIONRule-based classifier How a rule based classifier

works

207

Rule ordering schemes 211

How to build a rule based

classifier

212

Direct methods for rule

extraction

213

Indirect method for rule

extraction

221

Characteristics of rule basedclassifier

223

Nearest-neighbor

classifier

Algorithm 223

Characteristics Of Nearest

Neighbor Classifier

225

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UNIT - 4

6 ASSOCIATION ANALYSIS Problem Definition 328

Frequent Itemset

generation

The Apriori

Principal

333

Frequent Itemset

Generation in the

Apriori

Algorithm

335

Candidate

Generation and

Pruning

338

Support

Counting

342

ComputationalComplexity

345

Rule Generation Confidence

Based Pruning

350

Rule Generation

in Apriori

Algorithm

350

An Example:

CongressionalVoting Records

352

Compact

representation of

frequent itemsets

Maximal

Frequent

Itemsets

354

Closed Frequent

Itemsets

355

Alternative

methods for

generating

frequent itemsets

359

UNIT - 5

FP-Growth

algorithm

FP Tree

Representation

363

Frequent Itemset

Generation in FP

366

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Growth

Algorithm

Evaluation of

association

patterns

Objective

Measures of

Interestingness

371

Measure beyond

pairs of

Objective

measures of

Interestingness

binary variables

382

Simsons

Paradox

384

Effect of skewed

support

distribution

386

ASSOCIATION ANALYSIS

2:

Sequential

patterns.

Problem

Formulation

429

Sequential

Pattern

Discovery

431

Timing

Constraints

436

Alternative

Counting

Schemes

439


UNIT - 6

CLUSTER ANALYSIS Overview What Is Cluster Analysis 490

Different Types of 491

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Clustering

Different Types of Clusters 493

K-means The basic K-means

Algorithm

497

K-means: Additional issues 506

Bisecting K-Means 508

K-Means and Different

Types of Cluster

510

Strength and Weaknesses 510

K-means as an

Optimization Problem

513

Agglomerative

hierarchical

clustering

Basic Agglomerative

Hierarchical Clustering

Algorithm

516

Specific Techniques 518

The Launce-Williams

Formula for Cluster

524

Key issue in Hierarchical

Clustering

524

Strength & Weakness 526

DBSCAN Traditional Density:

Center-Based Approach

527

The DBSCAN Algorithm 528

Strengths and Weaknesses 530

Overview of

Cluster

Evaluation

Overview 533

Unsupervised Cluster

Evaluation Using Cohesion

and Separation

536

Unsupervised Cluster

Evaluation Using

Proximity Matrix

542

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Unsupervised Evaluation

of Hierarchical Clustering

544

Determining the correct

Number of Clusters

546

Clustering Tendency 547

Supervised Measures of

Cluster Validity

548

Assessing the Significance

of Cluster Validity

Measures

553

Hours

UNIT CHAPTER IN BOOK 2 TOPIC CONTENT PAGE NO.

UNIT - 7

FURTHER TOPICS IN

DATA MINING

Multidimensional

analysis and

descriptive

mining of

complex data

objects

Generalization of

Structured Data

592

Aggregation and

Approximation

in Spatial and

Multimedia Data

Generalization

593

Generalization ofObject Identifiers

and

Class/subclass

Hierarchies

594

Generalization of

Class

Composition

Hierarchies

595

Construction andMining of Object

Cubes

596

Generalization

Based Mining of

Plan Databases

by Divide and

596

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Conquer

Spatial data

mining

Spatial data Cube

Construction and

Spatial OLAP

601

Mining Spatial

Association and

Co-location

Patterns

605

Spatial

Clustering

Methods

606

Spatial

Classification

and Spatial TrendAnalysis

606

Mining Raster

Databases

607

Multimedia data

mining

Similarity Search

in Multimedia

Data

608

Multidimensional

Analysis of

Multimedia Data

609

Classification

and Predication

Analysis of

Multimedia Data

611

Mining

Association in

Multimedia Data

612

Audio & VideoData Mining

613

Text mining Text Data

Analysis and

Information

Retrieval

615

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Dimensionality

Reduction for

Text

621

Text Mining

Approach

624

Mining the WWW Mining the Web

page layout

structure

628-630

Mining the Web

link Structure to

Identify

Authoritative

Web Pages

631

Mining

Multimedia Data

on the Web

637

Automatic

Classification of

Web Documents

638

Web Usage

Mining

640


UNIT - 8

APPLICATIONS Data mining

applications

Data mining for

Financial Data

Analysis

649

Retail Industry 651

Telecommunication

Industry

652

Biological Data

Analysis

654

Other Scientific

Application

657

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Intrusion Detection 658

Data mining

system products

and research

prototypes

How to Choose a

Data mining

System

660

Examples of

Commercial Data

Mining Systems

663

Additional

themes on Data

mining

Theoretical

Foundation of Data

Mining

665

Statistical Data

Mining

666

Visual and AudioData Mining

667

Data Mining

Privacy and Data

Security

670

Social impact of

Data mining

Ubiquitous and

Invisible Data

Mining

675

Data Mining

Privacy and Data

Security

678

Trends in Data

mining

681

TEXT BOOKS:

1. Introduction to Data Mining - Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson

Education, 2007

2. Data Mining Concepts and Techniques - Jiawei Han and Micheline Kamber, 2

nd

Edition,Morgan Kaufmann, 2006.

REFERENCE BOOKS:

1. Insight into Data Mining Theory and Practice - K.P.Soman, Shyam Diwakar, V.Ajay, PHI, 2006.

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