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Core/Elective: Core Semester: 2 Credits: 4

Course Description

Data mining is the science of extracting hidden information from large datasets. This course offers clear and comprehensive introduction to both data mining theory and Practice. All major data mining techniques will be dealt with and how to apply these techniques in real problems are explained through case studies.

Course Objectives

Introduce the fundamental concepts of data and data analysis
Case based study of specific data mining tasks like Clustering, Classification, Regression, Pattern Discovery and Retrieval by Content.
Introduce algorithms for temporal data mining and spatial data mining.

Course Content

1. Classification and prediction- decision tree induction-bayesian classification-rule-based classification- neural networks-support vector machines-lazy learners-genetic algorithms- prediction-accuracy and error measures-ensemble methods- model selection

2. Cluster analysis- portioning methods- hierarchical methods- density based methods-grid based-model based-constraint based-clustering high dimensional data-outlier analysis

3. Mining Sequence patterns in transactional databases-scalable methods for mining sequential patterns- constrained based methods-HMM for biological sequence data

4. Temporal data types and pre processing-time series similarity measures-time series representation and summarization methods- time series classification and clustering techniques

5. Spatial data mining-spatial data cube construction-mining spatial association and co-location patterns-spatial clustering and classification methods-spatial trend analysis


1.Temporal Data mining –Theophano Mitsa, CRC Press 2010
2. Data mining concepts and techniques- Jiawei Han & Micheline Kamber , Elsevier (2008)
3. Data mining methods and Techniques: A B M Showkat Ali, Saleh A Wasimi, Cengage Learning (2009)
4. Introduction to Data mining with case studies: G.K Gupta PHI (2008)

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