Summary „Rough sets and Data mining” Vietnam national university in Hanoi, 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