MC9280 DATA MINING AND DATA WAREHOUSING Syllabus for 5th Sem MCA - Fifth semester - Regulation 2009 - Anna University


MC9280 DATA MINING AND DATA WAREHOUSING Syllabus for 5th Sem MCA - Fifth semester - Regulation 2009 - Anna University

MC9280
DATA MINING AND DATA WAREHOUSING
LT P C


3 0 0 3
UNIT I

9
Data Warehousing and Business Analysis: - 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 reporting Query tools and Applications Online Analytical Processing (OLAP) OLAP and Multidimensional Data Analysis.

UNIT II                                                                                                                               9
Data Mining: - Data Mining Functionalities Data Preprocessing Data Cleaning Data Integration and Transformation – Data Reduction Data Discretization and Concept Hierarchy Generation.
Association Rule Mining: - Efficient and Scalable Frequent Item set Mining Methods Mining Various Kinds of Association Rules Association Mining to Correlation Analysis
Constraint-Based Association Mining.

UNIT III                                                                                                                              9
Classification and Prediction: - Issues Regarding Classification and Prediction Classification by Decision Tree Introduction Bayesian Classification Rule Based Classification Classification by Back propagation Support Vector Machines – Associative Classification Lazy Learners – Other Classification Methods Prediction Accuracy and Error Measures Evaluating the Accuracy of a Classifier or Predictor Ensemble Methods Model Section.

UNIT IV                                                                                                                             9
Cluster Analysis: - Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods Partitioning Methods Hierarchical methods Density-Based Methods – Grid-Based Methods Model-Based Clustering Methods Clustering High- Dimensional Data Constraint-Based Cluster Analysis Outlier Analysis.

UNIT V                                                                                                                              9
Mining Object, Spatial, Multimedia, Text and Web Data:
Multidimensional Analysis and Descriptive Mining of Complex Data Objects Spatial
Data Mining Multimedia Data Mining Text Mining Mining the World Wide Web.

TOTAL : 45 PERIODS

REFERENCES
1.  Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques” Second
Edition,
2.  Elsevier, Reprinted 2008.
3.  Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP, Tata
McGraw – Hill Edition, Tenth Reprint 2007.
4.  K.P. Soman, Shyam Diwakar and V. Ajay Insight into Data mining Theory and
Practice, Easter Economy Edition, Prentice Hall of India, 2006.
5.  G.  K.  Gupta  Introduction  to  Data  Mining  with  Case  Studies,  Easter  Economy
Edition, Prentice Hall of India, 2006.
6.  Pang-Ning Tan, Michael Steinbach and Vipin Kumar Introduction to Data Mining, Pearson Education, 2007.
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