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## Summary

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**Summary**„Rough sets and Data mining” Vietnam national university inHanoi, College of technology, Feb.2006**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**Boolean reasoning methodology**• Monotone Boolean function • Implicant, prime implicant • Searching for minimal prime implicants of a monotone Boolean function**Data preprocessing and data cleaning**• Discretization methods • Data reduction methods • Missing values • Outlier elimination • Rough set methods for discretization and attribute reduction**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**Classification methods**• Instance-based classification techniques • Bayesian classifiers • Decision tree methods • Decision rules methods • Classifier evaluation techniques**Discernibility measure**• Applications of discernibility measure in • Feature selection • Discretization • Symbolic value grouping • Decision tree construction