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Old September 27th, 2017, 09:22 AM
Default Data Mining Syllabus RGPV

My sister is pursuing B.E Computer Science Course at Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV). She is in 8th Semester. She wants syllabus of ‘Data Mining and Knowledge Discovery’ subject of BE Computer Science 8th Semester Course. So someone is here who will provide syllabus of BE Course of Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV)?
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Old September 27th, 2017, 10:33 AM
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Join Date: May 2011
Default Re: Data Mining Syllabus RGPV

As you want syllabus of BE Computer Science 8th Semester Course of Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), so here I am providing syllabus:

Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) B.E 8th Semester Syllabus:
(Data Mining and Knowledge Discovery)

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.

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.

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.

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,.

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.
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