**MCA (Choice Based Credit System)**

**(Under Faculty of Science)**

**(Introduced from June 2016 and Onwards)**

**Semester-IV**

**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.

**Reference books: **

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