Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Database Systems: The Complete Book
Database Systems: The Complete Book
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Model of RSDM Implementation
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
A new rough sets model based on database systems
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
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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.