MC9280 DATA MINING AND DATA WAREHOUSING SYLLABUS | ANNA UNIVERSITY MCA 5TH SEM SYLLABUS REGULATION 2009 2011 2012-2013

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MC9280 DATA MINING AND DATA WAREHOUSING SYLLABUS | ANNA UNIVERSITY MCA 5TH SEM SYLLABUS REGULATION 2009 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY FIFTH SEMESTER M.C.A. (MASTER OF COMPUTER APPLICATIONS) DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,PREVIOUS YEAR QUESTION PAPERS,MODEL QUESTION PAPERS, CLASS NOTES, IMPORTANT 2 MARKS, 8 MARKS, 16 MARKS TOPICS. IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI,TIRUNELVELI,COIMBATORE), 2009 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009

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
31
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.