A Hunt for Reaching Horizon of Science



Introduction: Fundamentals of Data Mining, Kinds of Patterns can be mined, Technologies Used, Applications and Issues in Data Mining

Types of Data: Attribute types, Basic Statistical descriptions of Data, Measuring data Similarity and Dissimilarity

Data Preprocessing: Need of Preprocessing, Data Cleaning, Data Integration, Data Reduction, Data Transformation


Data Warehouse and OLAP: Data Warehouse, Data Warehouse Modeling, Data Warehouse Design and Usage, Data Warehouse Implementation, Data Generalization by Attribute-oriented induction


Mining Frequent Patterns, Associations and Correlations: Market Basket Analysis, Association rule mining, Frequent Item set mining methods, Pattern Evaluation methods, Constraint based frequent pattern mining, Mining Multilevel and Multidimensional patterns


Classification :  General approach to classification, Classification by Decision Tree Induction , Bayes Classification methods, Bayesian Belief Networks, Classification by Backpropogation,  Lazy Learners, Other Classification methods , Classification using Frequent patterns,  Model Evaluation  and selection


Cluster Analysis: Basic Clustering methods, Partitioning methods, Density –Based Methods, Grid-based methods, and Evaluation of Clustering, Outlier Analysis and Detection methods

 Data Mining Trends and Research Frontiers: Mining Complex Data Types, Data Mining Applications, Data Mining Trends


Suggested Reading:

1.Data Mining – Concepts and Techniques – Jiawei Han & Micheline Kamber and Jain Pei, Third Edition, India ( 2011).


1.Data Mining Introductory and advanced topics – Margaret H Dunham, Pearson  education

2.  Data Mining Techniques – Arun K Pujari, University Press.

3.  Data Warehousing in the Real World – Sam Anahory & Dennis Murray Pearson Edn 

4.  Data Warehousing Fundamentals – Paulraj Ponnaiah Wiley Student ed.

5. The Data Warehouse Life cycle Tool kit – Ralph Kimball Wiley student   edition.