OBJECTIVE:
To introduce concepts of data mining techniques and its applications in knowledge extraction from databases.
Data mining – Motivation – Importance - DM Vs KDD - DM Architecture - Data Types – DM Tasks –DM System Classification - Primitives of DM - Data Mining Query Language - DM Metrics - DM Applications - DM Issues – Social Implications of DM
Data Preprocessing: Summarization - Data cleaning - Data Integration and Transformation - Data Reduction - Discretization and Concept Hierarchy Generation
Mining Frequent Patterns – Frequent Itemset Mining Methods. Classification: Classification by Decision Tree Induction – Bayesian Classification – Rule based Classification - Prediction – Accuracy and Error Measures
Cluster Analysis – Types of Data in Cluster Analysis – Categorization of clustering Methods – Partition Methods - Outlier Analysis – Mining Data Streams – Social Network Analysis – Mining the World Wide Web
Data Warehousing: OLTP Vs OLAP - Multidimensional Data Model -DW Architecture Efficient Processing of OLAP queries - Metadata repository – DWH Implementation - OLAM
REFERENCES:
1. JiaweiHan ,MichelineKamber, "Data Mining: Concepts and Techniques", 2nd Edition, Elsevier India Private Limited,2008.
2. Margaret H. Dunham, "Data Mining: Introductory and Advanced Topics", Pearson Education, 2012.
3. K.P.Soman, ShyamDiwakar, V.Ajay, “Insight into Data Mining Theory & Practice, Prentice Hall India,2012,
4. G.H.Gupta, “Introduction to Data Mining with Case Studies”, 2nd Edition, PHI.
COURSE OUTCOME:
Students will be able to:
1. Explain the concepts in data mining and KDD, recognizing issues in Data Mining
2. Practice the preprocessing operations of Data
3. Define the methodologies in Data interpretation, transformation and reduction
4. Perform Association Rule Mining, Classify and Cluster the data sets into groups
