IT414

Course Name: 

Data Warehousing And Data Mining (IT414)

Programme: 

B.Tech (IT)

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 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, data reduction techniques, 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: Efficient methods for data cube computation, discovery driven exploration of data cubes, complex aggregation, attribute oriented induction for data generalization; Mining frequent patterns, associations and correlations: Basic concepts, efficient and scalable frequent itemset mining algorithms, mining various kinds of association rules – multilevel and multidimensional, association rule mining versus correlation analysis, constraint based association mining; Classification and prediction: Definition, decision tree induction, Bayesian classification, rule based classification and support vector machines, associative classification, lazy learners, prediction, accuracy and error measures; Cluster analysis: Definition, clustering algorithms – partitioning, hierarchical, density based, grid based and model based; Clustering high dimensional data, constraint based cluster analysis; Data mining on complex data and applications: Algorithms for mining of spatial data, multimedia data, text data; Data mining applications, social impacts of data mining, trends in data mining.

References: 

Han, J. and Kamber, M., “Data Mining - Concepts and Techniques”, 3rd Ed., Morgan Kaufmann Series, 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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