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Reliable Representations for Association Rules by Yue Xu; Yuefeng Li; Gavin Shaw is a Computer Science article available to read on EtoBox.

What is Reliable Representations for Association Rules about?

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as poss

Who reads Reliable Representations for Association Rules?

Author

Yue Xu; Yuefeng Li; Gavin Shaw

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0169-023X)

Published

2011

Language

EN

Field

Computer Science (Physical Sciences)