17MCA442 Data Warehousing and Data Mining syllabus for MCA



A d v e r t i s e m e n t

Module-1 Data warehousing and OLAP 8 hours

Data warehousing and OLAP

Data Warehouse basic concepts, Data Warehouse Modeling, Data Cube and OLAP : Characteristics of OLAP systems, Multidimensional view and Data cube, Data Cube Implementations, Data Cube operations, Implementation of OLAP and overview on OLAP Software.

Module-2 Data Mining and its Applications 8 hours

Data Mining and its Applications

Introduction, What is Data Mining, Motivating Challenges, Data Mining Tasks, Which technologies are used for data mining, Kinds of pattern that can be mined,Data Mining Applications, Data Preprocessing, Datacleaning, data integration, data reduction and data transformation.

Module-3 Association Analysis: Basic Concepts and Algorithms 8 hours

Association Analysis: Basic Concepts and Algorithms

Frequent Item set Generation, Rule Generation, Compact Representation of Frequent Item sets, Alternative methods for generating Frequent Item sets, FP Growth Algorithm, Evaluation of Association Patterns

Module-4 Classification : Methods, Improving accuracy of classification 8 hours

Classification : Methods, Improving accuracy of classification

Basics, General approach to solve classification problem, Decision Trees, Rule Based Classifiers, Nearest Neighbor Classifiers. Bayesian Classifiers, Estimating Predictive accuracy of classification methods, Improving accuracy of classification methods, Evaluation criteria for classification methods, Multiclass Problem.

Module-5 Clustering Techniques 8 hours

Clustering Techniques

Overview, Features of cluster analysis, Types of Data and Computing Distance, Types of Cluster Analysis Methods, Partitional Methods, Hierarchical Methods, Density Based Methods, Quality and Validity of Cluster Analysis

 

Question paper pattern:

  • The question paper will have ten questions.
  • Each full question consists of 16 marks.
  • There will be 2full questions (with a maximum of four sub questions) from each module.
  • Each full question will have sub questions covering all the topics under a module.
  • The students will have to answer 5 full questions, selecting one full question from each module.

 

Text Books:

1. Jiawei Han and MichelineKamber: Data Mining - Concepts and Techniques, 2nd Edition, Morgan Kaufmann Publisher, 2006.

2. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Addison- Wesley, 2005.

 

Reference Books:

1. Arun K Pujari: Data Mining Techniques University Press, 2nd Edition, 2009.

2. G. K. Gupta: Introduction to Data Mining with Case Studies, 3rd Edition, PHI, New Delhi, 2009.

3. Alex Berson and Stephen J.Smith: Data Warehousing, Data Mining, and OLAP Computing McGrawHill Publisher, 1997.

Last Updated: Tuesday, January 24, 2023

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