Efficient methods exist for discovering association rules fromlarge collections of data. The number of discovered rules can,however, be so large. At the same time it is well known that manydiscovered associations are ...Efficient methods exist for discovering association rules fromlarge collections of data. The number of discovered rules can,however, be so large. At the same time it is well known that manydiscovered associations are redundant or minor variations of others.Their existence may simply be due to chance rather than truecorrelation. Thus, those spurious and insignificant rules should beremoved. In this paper, we propose a novel technique to over- Comethis problem. The technique firstly introduces the newconcept-structure rule cover, and then present a Quantitative methodto prune redundant correlation patterns. The user can now obtain acomplete picture of the do- Main without being overwhelmed by a hugenumber of rules.展开更多
文摘Efficient methods exist for discovering association rules fromlarge collections of data. The number of discovered rules can,however, be so large. At the same time it is well known that manydiscovered associations are redundant or minor variations of others.Their existence may simply be due to chance rather than truecorrelation. Thus, those spurious and insignificant rules should beremoved. In this paper, we propose a novel technique to over- Comethis problem. The technique firstly introduces the newconcept-structure rule cover, and then present a Quantitative methodto prune redundant correlation patterns. The user can now obtain acomplete picture of the do- Main without being overwhelmed by a hugenumber of rules.
