AI460

Course Name: 

Data Mining (AI460)

Programme: 

B.Tech (AI)

Category: 

Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-0-2) 4

Content: 

Introduction to data mining: Motivation and significance of data mining, Data mining on what kind of data? , data mining functionalities, interestingness measures, classification of data mining system, major issues in data mining; Data pre-processing: Need, data summarization, data cleaning, data integration and transformation, Attribute subset selection methods: filter based and wrapper based methods, Information gain based, correlation based, Minimum redundancy maximum relevance based methods, data discretization and concept hierarchy generalization. Data warehouse and OLAP technology: multidimensional data model(s), data warehouse architecture, OLAP server types, data warehouse implementation, on-line analytical processing and mining; Data cube computation and data generalization. Mining frequent patterns, associations and correlations: Basic concepts, efficient and scalable frequent itemset mining algorithms: A-priori and FP Tree methods, mining various kinds of association rules – multilevel and multidimensional, association rule mining versus correlation analysis, constraint based association mining; Colossal item set Mining: Enumeration methods, Dynamic switching method, parallel method, sequential pattern mining; Bayesian classification, associative classification, lazy learners, grid based and density based clustering methods, Clustering high dimensional data; Data mining on complex data and applications: Algorithms for mining multimedia data, text data, multimodal data, biological sequence data; Data mining applications and trends in data mining.

References: 

Han, J. and Kamber, M., “Data Mining - Concepts and Techniques”, 3rd Ed., Morgan Kaufmann Series, (Elsevier), 2008.
Alex Berson , S. J. Smith, “Data Warehousing, Data Mining & OLAP” , McGraw Hill
Tan, P.N., Steinbach, M. and Kumar, V., “Introduction to Data Mining”, Addison Wesley Pearson, 2006
Pujari, A. K., “Data Mining Techniques”, 4th Ed., Sangam Books.
Oded Maimon, Lior Rokach, The Data Mining and Knowledge Discovery Handbook, Springer, 2005.
S. Weiss and N. Indurkhya, Predictive Data-Mining: A Practical Guide, Morgan Kaufmann, 1998
S. Weiss, N. Indurkhya, T. Zhang and F. Damerau, Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer, 2004.

Department: 

Information Technology
 

Contact us

Head of the Department,
Department of Information Technology,
National Institute of Technology Karnataka,
SurathkalP. O. Srinivasnagar, Mangalore - 575 025
Ph.:    +91-824-2474056
Email:  hodit [at] nitk [dot] edu [dot] in
 

Web Admin: Sowmya Kamath S

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