1)Introduction and Basic Concepts: What is Data Mining? Definition, Comparison of Database Management System and Data Mining, Motivation, Knowledge Discovery Vs Data Mining, Classifications, Context, Mining Problems, Challenges, Applications of Data Mining, Benefits of Data Mining, Data Mining process. 10 Hrs.
2)Knowledge Discovery: What is Knowledge Discovery, Knowledge in Discovery, Data for Data Mining, Data Resources, Meta Data, Data Representation, Data Quality, Features, Sampling, Data Summarization, Missing Data, and Incorrect Values. 6 Hrs.
3)Data Mining Techniques and Algorithms: Association Rules: What is an Association rule, Methods, A Priori Algorithm, Partition Algorithm, Pincer-Search Algorithm, Dynamic Item set Counting Algorithm, FP-tree Growth Algorithm, Discussion, Incremental Algorithm, Border Algorithm, Generalized Association Rule, Constraints
[Portions which are marked in red are not for CAT - 1]
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