RGTU/RGPV CS-7203 Data Mining & Knowledge Discovery SYLLABUS

Data Mining & Knowledge Discovery SYLLABUS Course code: CS-7203

Computer Science "CSE" 7th semester syllabus RGTU/RGPV Syllabus

Data Mining & Knowledge Discovery SYLLABUS: Course Content

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/Suggested Reading/ Books for Data Mining & Knowledge Discovery :

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,

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