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Old 3rd November 2020, 10:45 AM
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Default Sathyabama Institute of Science and Technology BE CSE SCSA3001 Data Mining and Data Warehousing Syllabus

Sathyabama Institute of Science and Technology BE CSE SCSA3001 Data Mining and Data Warehousing Syllabus

SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL OF COMPUTING

SCSA3001 DATA MINING AND DATA WAREHOUSING
L T P Credits Total Marks
3 0 0 3 100

UNIT 1 DATA MINING 9 Hrs.
Introduction - Steps in KDD - System Architecture – Types of data -Data mining functionalities - Classification of data mining
systems - Integration of a data mining system with a data warehouse - Issues - Data Preprocessing - Data Mining
Application

UNIT 2 DATA WAREHOUSING 9 Hrs.
Data warehousing components - Building a data warehouse - Multi Dimensional Data Model - OLAP Operation in the Multi-
Dimensional Model - Three Tier Data Warehouse Architecture - Schemas for Multi-dimensional data Model - Online
Analytical Processing (OLAP) - OLAP Vs OLTP Integrated OLAM and OLAP Architecture.

UNIT 3 ASSOCIATION RULE MINING 9 Hrs.
Mining frequent patterns - Associations and correlations - Mining methods - Finding Frequent itemset using Candidate
Generation - Generating Association Rules from Frequent Itemsets - Mining Frequent itemset without Candidate Generation
- Mining various kinds of association rules - Mining Multi-Level Association Rule-Mining MultiDimensional Association Rule-
Mining Correlation analysis - Constraint based association mining.

UNIT 4 CLASSIFICATION AND PREDICTION 9 Hrs.
Classification and prediction - Issues Regarding Classification and Prediction - Classification by Decision Tree Induction -
Bayesian classification - Baye’s Theorem - Naïve Bayesian Classification - Bayesian Belief Network - Rule based
classification - Classification by Back propagation - Support vector machines - Prediction - Linear Regression.

UNIT 5 CLUSTERING, APPLICATIONS AND TRENDS IN DATA MINING 9 Hrs.
Cluster analysis - Types of data in Cluster Analysis - Categorization of major clustering methods -Partitioning methods -
Hierarchical methods - Density-based methods - Grid-based methods - Model based clustering methods -Constraint Based
cluster analysis - Outlier analysis - Social Impacts of Data Mining- Case Studies: Mining WWW- Mining Text Database-
Mining Spatial Databases.
Max. 45 Hrs.

COURSE OUTCOMES
On completion of the course, student will be able to
CO1 - Assess Raw Input Data and process it to provide suitable input for a range of data mining algorithm.
CO2 - Design and Modeling of Data Warehouse.
CO3 - Discover interesting pattern from large amount of data.
CO4 - Design and Deploy appropriate Classification Techniques.
CO5 - Able to cluster high dimensional Data.
CO6 - Apply suitable data mining techniques for various real time applications.

TEXT / REFERENCE BOOKS
1. Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, 2nd Edition, Elsevier, 2007
2. Alex Berson and Stephen J. Smith, “ Data Warehousing, Data Mining & OLAP”, Tata McGraw Hill, 2007.
3. Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction To Data Mining”, Person Education, 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. Daniel T.Larose, “Data Mining Methods and Models”, Wile-Interscience, 2006.

END SEMESTER EXAMINATION QUESTION PAPER PATTERN
Max. Marks: 100 Exam Duration: 3 Hrs.
PART A: 10 Questions of 2 marks each-No choice 20 Marks
PART B: 2 Questions from each unit with internal choice, each carrying 16 marks 80 Marks
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