CS9264 DATA WAREHOUSING AND DATA MINING

CS9264 DATA WAREHOUSING AND DATA MINING

UNIT I

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

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

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

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

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.

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.