DATA MINING MADRAS UNIVERSITY SYLLABUS

Title of the Course/  Paper	DATA MINING
Elective	III Year & Sixth Semester	Credit: 5
Objective of the course	This course introduces the fundamental concepts of Data Mining.
Course outline	Unit1: Introduction: Data mining – Functionalities – Classification – Introduction to Data Warehousing – Data Preprocessing : Preprocessing the Data – Data cleaning – Data Integration and Transformation – Data Reduction
	Unit-2: Data Mining, Primitives, Languages and System Architecture: Data Mining – Primitives – Data Mining Query Language,.  Architectures of Data mining Systems.  Concept Description, Characterization and Comparison: Concept Description, Data Generalization and Summarization, Analytical Characterization, Mining Class Comparison – Statistical Measures.
	Unit 3: Mining Association Rules: Basics Concepts – Single Dimensional Boolean Association Rules  From  Transaction Databases, Multilevel Association Rules from transaction databases – Multi dimension Association Rules from Relational Database and Data Warehouses.
	Unit-4:  Classification and Prediction: Introduction – Issues – Decision Tree Induction – Bayesian Classification – Classification of Back Propagation.  Classification based on Concepts from Association Rule Mining – Other Methods.  Prediction – Introduction – Classifier Accuracy.
	Unit-5: Cluster Analysis:  Introduction – Types of Data in Cluster Analysis, Petitioning Methods – Hierarchical Methods Density Based Methods – GRID Based Method – Model based Clustering Method.

1. Recommended Texts

i.J.Han and  M. Kamber,2001,Data Mining Concepts and Techniques,Harcourt India Pvt. Ltd  - New Delhi.

2. Reference Books

i. K.P. Soman , Shyam Diwakar, V.Ajay ,2006, Insight into Data Mining Theory and Practice, Prentice Hall of India Pvt. Ltd - New Delhi.

3. Website, E-learning resources

    i   http:// www.academicpress.com

   ii. http://www.mkp.com