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Core/Elective: Elective Semester: 2 Credits: 3

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. Statistical descriptions of data-data visualization-measuring data similarity and dissimilarity-data preprocessing-data cleaning-data integration-data reduxtion-data transformation-data warehouse modeling-design-implementation-data cube technology-queries by data cube technology-multidimensional data analysis in Cube space

2. mining frequent patterns, associations and correlations – patten mining in multidimensional space- colossal patterns- approximate patterns- applications- Mining data streams-Mining Sequence patterns in transactional databases- mining sequence pattern in Biological Data

3. Classification and prediction- decision tree induction-bayesian classification-rule-based classification- neural networks-support vector machines-lazy learners-genetic algorithms-model evaluation-Cluster analysis- portioning methods- hierarchical methods- density based methods-grid based-probabilistic model based clustering- clustering high dimensional data-constraint based clustering- clustering high dimensional data-graph clustering methods

4. Outlier detection- outliers and outlier analysis- outlier detection methods-statistical approaches-proximity based approaches- clustering based approaches- classification based approaches-mining contextual and collective outliers- outlier detection in High-Dimensional data

5. Time series representation and summarization methods-mining time series data -Spatial data mining-spatial data cube construction-mining spatial association and co-location patterns-spatial clustering and classification methods-spatial trend analysis- Multimedia data mining-text mining- mining world wide web- trends in Data mining


1. Temporal Data mining –Theophano Mitsa, CRC Press 2010
2. Data mining concepts and techniques- Jiawei Han & Micheline Kamber, Jian Pei, Elsevier (2014)
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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