| MC9280 | DATA MINING AND DATA WAREHOUSING | LT P C |
| 3 0 0 3 | ||
| UNIT I | 9 |
Data Warehousing and Business Analysis: - Data warehousing Components â€"Building a
Data warehouse â€" Mapping the Data Warehouse to a Multiprocessor Architecture â€" DBMS Schemas for Decision Support â€" Data Extraction, Cleanup, and Transformation Tools â€"Metadata â€" reporting â€" Query tools and Applications â€" Online Analytical Processing (OLAP) â€" OLAP and Multidimensional Data Analysis.
UNIT IIÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 9
Data Mining: - Data Mining Functionalities â€" Data Preprocessing â€" Data Cleaning â€" Data Integration and Transfor mation â€" Data Reduction â€" Data Discretization and Concept Hierarchy Generation.
Association Rule Mining: - Efficient and Scalable Frequent Item set Mining Methods â€" Mining Various Kinds of Association Rules â€" Association Mining to Correlation Analysis
â€" Constraint-Based Association Mining.
UNIT IIIÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 9
Classification and Prediction: - Issues Regarding Classification and Prediction â€" Classification by Decision Tree Introduction â€" Bayesian Classification â€" Rule Based Classification â€" Classification by Back propagation â€" Support Vector Machines â€" Associative Classification â€" Lazy Learners â€" Other Classification Methods â€" Prediction â€" Accuracy a< /span>nd Error Measures â€" Evaluating the Accuracy of a Classifier or Predictor â€" Ensemble Methods â€" Model Section.
UNIT IVÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 9
Cluster Analysis: - Types of Data in Cluster Analysis â€" A Categorization of Major Clustering Methods â€" Partitioning Methods â€" Hierarchical methods â€" Density-Based Methods â€" Grid-Based Methods â€" Model-Based Clustering Methods â€" Clustering High- Dimens
UNIT VÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 9
Mining Object, Spatial, Multimedia, Text and Web Data:
Multidimensional Analysis and Descriptive Mining of Complex Data Objects â€" Spatial
Data Mining â€" Multimedia Data Mining â€" Text Mining â€" Mining the World Wide Web.
TOTAL : 45 PERIODS
REFERENCES
1.  Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques†Second
Edition,
2. Â Elsevier, Reprinted 2008.
3.  Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAPâ€, Tata
McGraw â€" Hill Edition, Tenth Reprint 2007.
4.  K.P. Soman, Shyam Diwakar and V. Ajay “Insight into Data mining Theory and
Practiceâ€, Easter Economy Edition, Prentice Hall of India, 2006.
5.  G.  K.  Gupta  “Introduction  to  Data  Mining  with  Case  Studiesâ€,  Easter  Economy
Edition, Prentice Hall of India, 2006.
6.  Pang-Ning Tan, Michael Steinbach and Vipin Kumar “Introduction to Data Miningâ€, Pearson Education, 2007.
0 comments:
Post a Comment