Tuesday, July 10, 2012

CS9027-DATA WAREHOUSING AND DATA MINING-ANNA UNIVERSITY SYLLABUS IT


CS9027-DATA WAREHOUSING AND DATA MINING-ANNA UNIVERSITY SYLLABUS IT


CS9027                          DATA WAREHOUSING AND DATA MINING                  L T P C
3  0 0  3
AIM:
To serve as an introductory  course to under graduate students with an emphasis on the design aspects of Data Mining and Data Warehousing

OBJECTIVE:

This course has been designed with the following objectives:

·             To introduce the concept of data mining with in detail coverage of basic tasks, metrics, issues, and implication. Core topics like classification, clustering and association rules are exhaustively dealt with.
·             To  introduce  the  concept  of  data  warehousing   with  special  emphasis  on architecture and design.

UNIT I             DATA WAREHOUSING                                                                          10
Data warehousing Components Building a Data warehouse –- Mapping the Data Warehouse to a Multiprocessor Architecture DBMS Schemas for Decision Support Data Extraction, Cleanup, and Transformation Tools Metadata.


UNIT II            BUSINESS ANALYSIS                                                                             8
Reporting  and  Query  tools  and  Applications   Tool  Categories   The  Need  for Applications Cognos Impromptu Online Analytical Processing (OLAP) Need Multidimensional  Data  Model   OLAGuidelines   Multidimensional  versus Multirelational OLAP Categories of Tools   OLAP Tools and the Internet.

UNIT III           DATA MINING                                                                                           8
Introduction Data Types of Data Data Mining Functionalities Interestingness of Patterns Classification of Data Mining Systems Data Mining Task Primitives Integration of a Data Mining System with a Data Warehouse Issues Data Preprocessing.

UNIT IV           ASSOCIATION RULE MINING AND CLASSIFICATION                     11
Mining Frequent Patterns, Associations and Correlations Mining Methods Mining Various  Kinds  of  Associatio Rules   Correlatio Analysis   Constrain Based Association Mining Classification and Prediction - Basic Concepts - Decision Tree Induction   - Bayesian Classification Rule Based Classification Classification by Backpropagation    Support  Vector  Machines   Associative  Classification   Lazy Learners Other Classification Methods - Prediction


UNIT V            CLUSTERING AND APPLICATIONS AND TRENDS IN DATA
MINING                                                                                                                        8
Cluster Analysis - Types of Data Categorization  of Major Clustering  Methods  - K- means Partitioning Methods Hierarchical Methods - Density-Based Methods Grid Based  Methods   Model-Based  Clustering  Methods   Clustering  High  Dimensional Data    -  Constraint   Based  Cluster  Analysis   Outlier  Analysis   Data  Mining Applications.



TEXT BOOKS:


TOTAL:45 PERIODS



1.   Alex Berson and Stephen J. Smith, Data Warehousing,  Data Mining & OLAP, Tata McGraw Hill Edition, Tenth Reprint 2007.
2.   Jiawei  Han  and  Micheline  Kamber,  Data  Mining  Concepts  and  Techniques,
Second Edition, Elsevier, 2007.

REFERENCES:

1.   Pang-Ning  Tan,  Michael  Steinbach  and  Vipin  Kumar,   Introduction  To  Data
Mining, Person Education, 2007.
2.   K.P. Soman, Shyam Diwakar and V. Ajay , Insight into Data mining Theory and
Practice, Easter Economy Edition, Prentice Hall of India, 2006.
3.   G. K. Gupta, Introduction  to Data Mining with Case Studies, Easter Economy
Edition, Prentice Hall of India, 2006.
4.   Soumendr Mohanty Data   Warehousin Design Developmen an Best
Practices, Tata McGraw Hill Edition, 2006.


CLICK HERE FOR ALL SUBJECTS

7/10/2012 12:21:00 PM

0 comments:

Post a Comment

Related Posts Plugin for WordPress, Blogger...