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Data Reduction Based on Rough Set Theory and Hierarchic Analysis

ZHANG Xue-feng,TIAN Xiao-dong,ZHANG Qing-ling(School of Sciences,Northeastern University,Shenyang 110004,China.)  
To find latent knowledge rules efficently and quickly from a great deal of information,the studies on data mining(DM) and knowledge discovery of database(KDD) become more popular and profound.Combining the rough set theory with hierarchic analysis model in view of their characteristics and introducing the definition of importance into non-core attributes,a new data reduction algorithm is proposed using a simple discriminative matrix.The validity and completeness of the algorithm is proved,by which an ideographic problem is common in medical treatment decision-making system is successfully solved.The results reveal that the algorithm is able to mine the knowledge and rules effectively and improve the rationality of data reduction.
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