High frequent value reduct in very large databases

Abstract

One of the main contributions of rough set theory to data mining is data reduction. There are three reductions: attribute (column) reduction, row reduction, and value reduction. Row reduction is merging the duplicate rows. Attribute reduction is to find important attributes. Value reduction is to reduce the decision rules to a logically equivalent minimal length. Most recent attentions have been on finding attribute reducts. Traditionally, the value reduct has been searched through the attribute reduct. This paper observes that this method may miss the best value reducts. It also revisits an old rudiment idea [11], namely, a rough set theory on high frequency data: The notion of high frequency value reduct is extracted in a bottom-up fashion without finding attribute reducts. Our method can discover concise and important decision rules in large databases, and is described and illustrated by an example. © Springer-Verlag Berlin Heidelberg 2007.