CS2032 DATA WAREHOUSING AND DATA MINING Syllabus

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  -  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.
           
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.