MCA 501: DataWarehousing And DataMining

Lectures: 4 Periods/Week Sessional Marks: 30
University Exam: 3 Hours University Examination Marks: 70


UNIT-I
Warehouse
What is it, Who Need It, and Why?, Things to Consider, Managing the Data Warehouse, Data Warehouse Design Methodology, Data Marts and Start Schema Design, Fundamentals of ETL Architecture, Partitioning Data, Indexing Data.

UNIT-II
Data mining
Introduction, Data mining on what kind of data , Data mining functionalities classification of Data mining systems, Major issues in Data mining.
Mining Association rules in large databases -
Association rule mining, Mining single- Dimensional Boolean association rules from Transactional databases, Mining multi- Dimensional Association rules fromrelational Databases and DataWarehouses.

UNIT-III
Classification and Prediction
Introduction classification by decision tree induction, Bayesian Classification. Other classification methods, classification by back propagation, Prediction, classifier accuracy.

UNIT-IV
Cluster analysis
Introduction types of data in cluster analysis a categorization of major clustering methods portioning methods, hierarchical methods, Density based methods,: DBSCAN, Grid-based method : STRING , Model based clustering method: Statistical Approach, outlier analysis.

Text Books

  1. Michael Corey, Michael Abbey, Ian Abramson, Ben Taub, “Oracle 8i Data Warehousing”, TMH (For Unit-I).
  2. Jiawei Han Micheline Kamber, “Data mining & Techniques”, Morgan Kaufmann Publishers (Unit-II to IV).
Reference Books
  1. S.N.Sivanandam, S.Sumathi, “Data Mining – Concepts, Tasks and Techniques”,Thomson (2006).
  2. Ralph Kimball, “The DataWarehousing Toolkit”,Wiley.
  3. Margaret H. Dunham, “Data mining - Introductory and advance topics”,Pearson Education.
  4. D.Hand, H. Mannila and P.Smyth, “Principles of Data mining”, PHI (2001).