CP7025 DATA MINING TECHNIQUES SYLLABUS FOR ME 3RD SEM CSE

ANNA UNIVERSITY, CHENNAI

REGULATIONS - 2013
CP7025 DATA MINING TECHNIQUES SYLLABUS

ME 3RD SEM COMPUTER SCIENCE AND ENGINEERING SYLLABUS

CP7025 DATA MINING TECHNIQUES SYLLABUS

UNIT I INTRODUCTION TO DATA MINING

Introduction to Data Mining – Data Mining Tasks – Components of Data Mining Algorithms – Data Mining supporting Techniques – Major Issues in Data Mining – Measurement and Data – Data Preprocessing – Data sets

UNIT II OVERVIEW OF DATA MINING ALGORITHMS

Overview of Data Mining Algorithms – Models and Patterns – Introduction – The Reductionist viewpoint on Data Mining Algorithms – Score function for Data Mining Algorithms- Introduction – Fundamentals of Modeling – Model Structures for Prediction – Models for probability Distributions and Density functions – The Curve of Dimensionality – Models for Structured Data – Scoring Patterns – Predictive versus Descriptive score functions – Scoring Models with Different Complexities – Evaluation of Models and Patterns – Robust Methods.

UNIT III CLASSIFICATIONS

Classifications – Basic Concepts – Decision Tree induction – Bayes Classification Methods – Rule Based Classification – Model Evaluation and Selection – Techniques to Improve Classification Accuracy – Classification: Advanced concepts – Bayesian Belief Networks- Classification by Back Propagation – Support Vector Machine – Classification using frequent patterns.

UNIT IV CLUSTER ANALYSIS

Cluster Analysis: Basic concepts and Methods – Cluster Analysis – Partitioning methods – Hierarchical methods – Density Based Methods – Grid Based Methods – Evaluation of Clustering – Advanced Cluster Analysis: Probabilistic model based clustering – Clustering High – Dimensional Data – Clustering Graph and Network Data – Clustering with Constraints.

UNIT V ASSOCIATION RULE MINING AND VISUALIZATION

Association Rule Mining – Introduction – Large Item sets – Basic Algorithms – Parallel and Distributed Algorithms – Comparing Approaches – Incremental Rules – Advanced Association Rule Techniques – Measuring the Quality of Rules – Visualization of Multidimensional Data – Diagrams for Multidimensional visualization – Visual Data Mining – Data Mining Applications – Case Study: WEKA.

TOTAL: 45 PERIODS

REFERENCE S:

1. Jiawei Han, Micheline Kamber , Jian Pei, “Data Mining: Concepts and Techniques”, Third Edition (The Morgan Kaufmann Series in Data Management Systems), 2012.

2. David J. Hand, Heikki Mannila and Padhraic Smyth “Principles of Data Mining” (Adaptive Computation and Machine Learning), 2005

3. Margaret H Dunham, “Data Mining: Introductory and Advanced Topics”, 2003

4. Soman, K. P., Diwakar Shyam and Ajay V. “Insight Into Data Mining: Theory And Practice”, PHI, 2009.