Data Mining and Knowledge Discovery

Unit-I
Introduction, to Data warehousing, needs for developing data Warehouse, Data warehouse
systems and its Components, Design of Data Warehouse, Dimension and Measures, Data
Marts:-Dependent Data Marts, Independents Data Marts & Distributed Data Marts, Conceptual
Modeling of Data Warehouses:-Star Schema, Snowflake Schema, Fact Constellations.
Multidimensional Data Model & Aggregates.
Unit-II
OLAP, Characteristics of OLAP System, Motivation for using OLAP, Multidimensional View and
Data Cube, Data Cube Implementations, Data Cube Operations, Guidelines for OLAP
Implementation, Difference between OLAP & OLTP, OLAP Servers:-ROLAP, MOLAP, HOLAP
Queries.
UNIT-III
Introduction to Data Mining, Knowledge Discovery, Data Mining Functionalities, Data Mining
System categorization and its Issues. Data Processing :- Data Cleaning, Data Integration and
Transformation. Data Reduction, Data Mining Statistics. Guidelines for Successful Data Mining.
Unit-IV
Association Rule Mining:-Introduction, Basic, The Task and a Naïve Algorithm, Apriori
Algorithms, Improving the efficiency of the Apriori Algorithm, Apriori-Tid, Direct Hasing and
Pruning(DHP),Dynamic Itemset Counting (DIC), Mining Frequent Patterns without Candidate
Generation(FP-Growth),Performance Evaluation of Algorithms,.
Unit-V
Classification:-Introduction, Decision Tree, The Tree Induction Algorithm, Split Algorithms Based
on Information Theory, Split Algorithm Based on the Gini Index, Overfitting and Pruning,
Decision Trees Rules, Naïve Bayes Method.
Cluster Analysis:- Introduction, Desired Features of Cluster Analysis, Types of Cluster Analysis
Methods:- Partitional Methods, Hierarchical Methods, Density- Based Methods, Dealing with
Large Databases. Quality and Validity of Cluster Analysis Methods.


References:
1. Berson: Data Warehousing & Data Mining &OLAP , TMH
2. Jiawei Han and Micheline Kamber, Data Mining Concepts & Techniques,
Elsevier Pub.
3. Arun.K.Pujari, Data Mining Techniques, University Press.
4. N.P Gopalan: Data Mining Technique & Trend, PHI
5. Hand, Mannila & Smith: Principle of Data Mining, PHI
6. Tan, Introduction to Data Mining, Pearson Pub.