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BAHUBALI COLLEGE OF ENGINEERING, SHRAVANABELAGOLA

Lesson/Session Plan Template

Department of Information Science & Engineering

Sub code: 06IS74

Sub: DATA MINING Sem: VII

S

N

Date Content Activity

UNIT - 1

INTRODUCTION, DATA 1: What is Data Mining? Motivating Challenges; The origins of data

mining; Data Mining Tasks. Types of Data; Data Quality.

6 Hours

1 What is Data Mining? Participation

&discussion

2 Motivating Challenges;

3 The origins of data mining; Explain with

an example

4 Data Mining Tasks. Discussions5 Types of Data;

6 Types of Data Discussions

7 Data Quality.

Measurement & data Collection Issues

Issues Related To Application

8 QP

Assignment

UNIT 2

DATA 2: Data Preprocessing; Measures of Similarity and Dissimilarity

6 Hours

9 Data Preprocessing

Aggregation

Sampling

Participation

&discussion

10 Data Preprocessing cont..

Dimensionality Reduction

Feature Subset Selection

Feature Creation

Discussions

11 Data Preprocessing cont

Discretization & Binarization

Variable Transformation

Explain with

an example

12 Measures of Similarity and Dissimilarity

Basics

Similarity & Dissimilarity between Simple Attributes

Dissimilarities Between Data Objects

13 Measures of Similarity and Dissimilarity

Similarities Between Data Objects Examples if Proximity Measures

Explain with

an example

14 Issues in Proximity Calculation

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Selecting The Right Proximity Measure

15 Question Paper

16 Question Paper

UNIT - 7

FURTHER TOPICS IN DATA MINING: Multidimensional analysis and descriptive mining of

complex data objects; Spatial data mining; Multimedia data mining; Text mining; Mining the WWW.Outlier analysis.

7 Hours

17 Multidimensional analysis and descriptive mining of complex data

objects

Generalization of Structured Data

Aggregation and Approximation in Spatial and Multimedia

Data Generalization

Generalization of Object Identifiers and Class/subclass

Hierarchies

Participation

&discussion

18 Multidimensional analysis and descriptive mining of complex data

objects cont

Generalization of Class Composition Hierarchies

Construction and Mining of Object Cubes

Generalization Based Mining of Plan Databases by Divide

and Conquer

19 Spatial data mining;

Spatial data Cube Construction and Spatial OLAP

Mining Spatial Association and Co-location Patterns

Discussions

20 Spatial Clustering Methods

Spatial Classification and Spatial Trend Analysis

21 Multimedia data mining; Mining Raster Databases

Similarity Search in Multimedia Data

Multidimensional Analysis of Multimedia Data

22 Multimedia data mining; Mining Raster Databases cont..

Classification and Predication Analysis of Multimedia Data

Mining Association in Multimedia Data

Audio & Video Data Mining

23 Text mining Text Data Analysis and Information Retrieval

Dimensionality Reduction for Text

Text Mining Approach

24 Mining the WWW.

Mining the Web page layout structure Mining the Web link Structure to Identify Authoritative

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Web Pages

25 Mining Multimedia Data on the Web

Automatic Classification of Web Documents

Web Usage Mining

UNIT - 8

APPLICATIONS: Data mining applications; Data mining system products and research prototypes;

Additional themes on Data mining; Social impact of Data mining; Trends in Data mining.

6 Hours

26 Data mining applications;

Data mining for Financial Data Analysis

Retail Industry

Telecommunication Industry

Participation

&discussion

27 Biological Data Analysis

Other Scientific Application

Intrusion Detection

28 Data mining system products and research prototypes;

How to Choose a Data mining System

Examples of Commercial Data Mining Systems

Explain with

an example

29 Additional themes on Data mining

Theoretical Foundation of Data Mining

Statistical Data Mining

Discussions

30 Visual and Audio Data Mining

Data Mining Privacy and Data Security

31 Social impact of Data mining;

Ubiquitous and Invisible Data Mining

Data Mining Privacy and Data Security

32 Trends in Data mining.

33 Question Bank Discussions

UNIT 3

CLASSIFICATION: Preliminaries; General approach to solving a classification problem; Decision

tree induction; Rule-based classifier; Nearest-neighbor classifier.

8 Hours

34 General approach to solving a classification problem; Participation

&discussion

35 Decision tree induction;

How a Decision Tree Works

How To Build A Decision Tree

Method for expressing attribute test conditions

Discussions

36 Decision tree induction cont Discussions

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Measure for selecting the best split

Algorithm for decision tree induction

An example : web robot detection

Characteristics Of decision tree induction

37 Rule-based classifier

How a rule based classifier works

Rule ordering schemes

How to build a rule based classifier

Discussions

38 Rule-based classifier cont..;

Direct methods for rule extraction

Indirect method for rule extraction

Characteristics of rule based classifier39 Nearest-neighbor classifier.

Algorithm

Characteristics Of Nearest Neighbor Classifier

Discussions

40 Question Paper Assignement

UNIT - 4

ASSOCIATION ANALYSIS 1: Problem Definition; Frequent Itemset generation; Rule

Generation; Compact representation of frequent itemsets; Alternative methods for generating frequent

itemsets.

6 Hours

41 Problem Definition; Participation&discussion

42 Frequent Itemset generation;

The Apriori Principal

Frequent Itemset Generation in the Apriori Algorithm

Candidate Generation and Pruning

Support Counting

Computational Complexity

43 Rule Generation;

Confidence Based Pruning

Rule Generation in Apriori Algorithm

An Example: Congressional Voting Records

Discussions

44 Compact representation of frequent itemsets;

Maximal Frequent Itemsets

Closed Frequent Itemsets

45 Alternative methods for generating frequent itemsets. Discussions

46 Alternative methods for generating frequent itemsets.

47 Question paper AssignmentUNIT - 5

ASSOCIATION ANALYSIS 2: FP-Growth algorithm, Evaluation of association patterns; Effect

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of skewed support distribution; Sequential patterns.

6 Hours

48 FP-Growth algorithm, FP Tree Representation

Frequent Itemset

Generation in FP Growth Algorithm

Participation

&discussion

49 Evaluation of association patterns;

Objective Measures of Interestingness

Measure beyond pairs of Objective measures of

Interestingness binary variables

Simsons Paradox

Discussions

50 Effect of skewed support distribution;

51 Problem Formulation

Sequential Pattern Discovery

Timing Constraints

Alternative Counting Schemes

Explain with

an example

52 Sequential patterns

53 Question paper Assignment

UNIT - 6

CLUSTER ANALYSIS: Overview, K-means, Agglomerative hierarchical clustering, DBSCAN,

Overview of Cluster Evaluation.

7 Hours

54 Overview,

What Is Cluster Analysis

Different Types of Clustering

Different Types of Clusters

Participation

&discussion

55 K-means,

The basic K-means Algorithm

K-means: Additional issues

Bisecting K-Means

K-Means and Different Types of Cluster

Strength and Weaknesses

K-means as an Optimization Problem

56 Agglomerative hierarchical clustering

Basic Agglomerative Hierarchical Clustering Algorithm

Specific Techniques

The Launce-Williams Formula for Cluster

Key issue in Hierarchical Clustering Strength & Weakness

Discussions

57 DBSCAN

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Traditional Density: Center-Based Approach

The DBSCAN Algorithm

Strengths and Weaknesses

58 Overview of Cluster Evaluation.

Overview

Unsupervised Cluster Evaluation Using Cohesion and

Separation

Unsupervised Cluster Evaluation Using Proximity Matrix

Unsupervised Evaluation of Hierarchical Clustering

Discussions

59 Overview of Cluster Evaluation.

Determining the correct Number of Clusters

Clustering Tendency

Supervised Measures of Cluster Validity

Assessing the Significance of Cluster Validity Measures

60 Question paper Assignment

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, 2nd

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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