Introduction to Data Mining Systems – Knowledge Discovery Process – Data Mining Techniques – Issues – applications- Data Objects and attribute types, Statistical description of data, Data Preprocessing – Cleaning, Integration, Reduction, Transformation and discretization, Data Visualization, Data similarity and dissimilarity measures.
Basic Concepts: Data Warehousing: A multitier Architecture, Data warehouse models: Enterprise warehouse, Data mart and virtual warehouse, Extraction, Transformation and loading, Data Cube: A multidimensional data model, Stars, Snowflakes and Fact constellations: Schemas for multidimensional Data models, Dimensions: The role of concept Hierarchies, Measures: Their Categorization and computation, Typical OLAP Operations.
Market Basket Analysis – Frequent Item Set Mining methods – Apriori algorithm – Generating Association Rules – A Pattern Growth Approach – Association Analysis to Correlation Analysis – Explore Weka and run Apriori algorithm with different support and confidence values (Supermarket dataset)
Basic concepts – Decision Tree Induction – Bayes Classification Methods – Rule based Classification – Model Evaluation and Selection – Techniques to improve Classification Accuracy – Classification by Back propagation- Support Vector Machines – Lazy Learners- Genetic Algorithm – Experiments with Weka (Iris plants dataset)
Basic issues in clustering – Partitioning methods: K-means, K-Medoids– Agglomerative Hierarchical Clustering – DBSCAN – Cluster Evaluation – Density Based Clustering – Grid Based Methods – Evaluation of clustering – Explore clustering techniques available in Weka (Breast cancer dataset)
Reference Book:
M Sudeep Elayidom, “Data Mining and Warehousingâ€, 1st Edition, 2015, Cengage Learning India Pvt. Ltd. Charu C. Aggarwal, Data Mining: The Textbook, Springer, 2015. G. K. Gupta, Introduction to Data Mining with Case Studies, Easter Economy Edition, Prentice Hall of India, 2014. Zaki and Meira, Data Mining and Analysis Fundamental Concepts and Algorithms, 2014 Pang-Ning Tan and Michael Steinbach, “Introduction to Data Miningâ€, Addison Wesley, 2006
Text Book:
J. Han and M. Kamber, Data Mining: Concepts and Techniques, Third Edition, Morgan Kaufman, 2013. Dunham M H, “Data Mining: Introductory and Advanced Topicsâ€, Pearson Education, New Delhi, 2003.
