MCA (Choice Based Credit System)
(Under Faculty of Science)
(Introduced from June 2016 and Onwards)
IT42: Data Mining
Internal Marks -20 External Marks-80 Theory-04 h/week
Unit I (15)
Data warehouse and OLAP technology: Data warehouse concepts, A multidimensional data model, Data warehouse architecture, From data warehousing to data mining. Introduction: Data mining concepts, Data mining functionalities, classification of data mining systems, Integration of data mining system with a database or data warehouse system, major issues in data mining, Data Preprocessing: Descriptive data summarization, data cleaning, data integration and transformation, data reduction, data discretization and concept hierarchy generation
Unit II (15)
Classification techniques: Classification: Preliminaries, general approach to solve classification problem, Decision tree induction, Rule-based classifier, Nearest-Neighbor classifier, Bayesian Classifiers, Support Vector Machine.
Unit III (15)
Association analysis: Problem definition, Frequent Itemset Generation, Apriori Principle, apriori algorithm, Maximal Frequent itemset, closed frequent itemset. FP-growth algorithm, Sequential Patterns, Infrequent Patterns.
Unit IV (15)
Cluster analysis: Introduction, Types of Clustering, Types of Clusters. K-means algorithm, Agglomerative Hierarchical Clustering, DBSCAN, Prototype based clustering and Density based clustering, Web Mining: Introduction, Web content Mining, Web structure Mining, Web Usage Mining. Introduction and practical on R and Weka.
1) Introduction to Data Mining – Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson education.
2) Data Mining concepts and techniques --- Jiawei Han and Micheline Kamber , Elsevier
3) Data Mining: Introductory and Advanced Topics - Margaret H. Dunham, Pearson education
4) Hands-On Programming with R, Garrett Grolemund
5) Beginning R, Dr Mark Gardener
6) An Introduction to the WEKA Data Mining, Zdravko Markov, “Ingrid Russell
7) Instant Weka How-to, Bostjan Kaluza
8) Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank
- Teacher: Vijay Kumbhar CSD