Go Back   2020-2021 StudyChaCha > StudyChaCha Discussion Forum > General Topics





  #1  
Old September 27th, 2017, 09:22 AM
Unregistered
Guest
 
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)?
Reply With Quote
Other Discussions related to this topic
Thread
IIT Madras Data Mining
RGPV CSE 7th Sem Syllabus
VTU Data Mining Notes
Syllabus Of BE 6th Sem RGPV
RGPV Third Sem Syllabus
RGPV BCE Syllabus
RGPV IT 7TH SEM Syllabus
New Syllabus Of RGPV
RGPV Data Communication Syllabus
IIT Bombay Data Mining
Data Mining And Warehousing
IIT Kharagpur Data Mining
Power Grid Data Mining
ICFAI MBA Data Warehousing and Data Mining (MB3G1IT) Paper
MBA Data Mining
DOEACC - C Level, Data Warehousing And Mining Exam Papers
Data Mining M Sc is Osmania
MU BE in IT 7th Sem. Data Warehousing, Mining and Business Intelligence Exam Papers
RAS Main (Mining Engineering) syllabus
PhD Research course in Data Mining






  #2  
Old September 27th, 2017, 10:33 AM
Super Moderator
 
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)
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.
__________________
Answered By StudyChaCha Member
Reply With Quote
Reply


Reply to this Question / Ask Another Question
Your Username: Click here to log in

Message:
Options



All times are GMT +6.5. The time now is 06:25 AM.


Powered by vBulletin® Version 3.8.11
Copyright ©2000 - 2020, vBulletin Solutions, Inc.
Search Engine Friendly URLs by vBSEO 3.6.0 PL2

1 2 3 4 5 6 7 8