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  1. Summary „Rough sets and Data mining” Vietnam national university inHanoi, College of technology, Feb.2006

  2. Main topics: • Definition, principles and functionalities of data mining systems • Rough sets methodology to concept approximation and data mining • Boolean reasoning approach to problem solving • Data preprocessing and data cleaning methods • Association rules • Classification methods

  3. Boolean reasoning methodology • Monotone Boolean function • Implicant, prime implicant • Searching for minimal prime implicants of a monotone Boolean function

  4. Data preprocessing and data cleaning • Discretization methods • Data reduction methods • Missing values • Outlier elimination • Rough set methods for discretization and attribute reduction

  5. Association rules • Definition, possible applications • Apriori search for frequent patterns and association rules • Modifications of apriori algorithms: hash tree, Apriori-Tid, Apriori-Hybrid • FP-tree method • Relationship between association rule and rough set methods

  6. Classification methods • Instance-based classification techniques • Bayesian classifiers • Decision tree methods • Decision rules methods • Classifier evaluation techniques

  7. Discernibility measure • Applications of discernibility measure in • Feature selection • Discretization • Symbolic value grouping • Decision tree construction