TAGS:-RGPV COMPUTER SCIENCE 7TH SEM SYLLABUS I DOWNLOAD RGPV COMPUTER SCIENCE 7TH SEM ELECTIVE SUBJECT SYLLABUS I DOWNLOAD RGPV COMPUTER SCIENCE COMPLETE SYLLABUS I RGTU COMPUTER SCIENCE ENGINEERING SYLLABUS FOR ELECTIVE SUBJECTS
RGPV COMPUTER SCIENCE DATA MINING AND KNOWLEDGE DISCOVERY(CS-7203)
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.
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, HOLAPQueries.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.
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,.
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.
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 Books:
1. Berson: Data Warehousing & Data Mining &OLAP , TMH2. 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
No comments:
Post a Comment