CS2032 DATA WAREHOUSING AND DATA MINING SYLLABUS | ANNA UNIVERSITY BE CSE 7TH SEMESTER SYLLABUS REGULATION 2008 2011-2012

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CS2032 DATA WAREHOUSING AND DATA MINING SYLLABUS | ANNA UNIVERSITY BE CSE 7TH SEMESTER SYLLABUS REGULATION 2008 2011-2012 BELOW IS THE ANNA UNIVERSITY SEVENTH SEMESTER B.E. COMPUTER SCIENCE AND ENGINEERING DEPARTMENT SYLLABUS IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011-2012 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI,TIRUNELVELI,COIMBATORE), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009


CS2032 DATA WAREHOUSING AND DATA MINING L T P C
3 0 0 3
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 – OLAP Guidelines – 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 Association Rules – Correlation Analysis – Constraint 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 - Kmeans
– 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.
TOTAL: 45 PERIODS
TEXT BOOKS:
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. Daniel T.Larose, “Data Mining Methods and Models”, Wile-Interscience, 2006.