Title

A New Interestingness Measure Of Association Rules

Abstract

Discovering association rules is one of the most important tasks in data mining. The classical model of association rides mining is support-confidence, the interestingness measure of which is the confidence measure. The classical interestingness measure in association rules have existed some disadvantage. In this paper, some problem of interestingness measure on the classical association rules model have been analyzed, and then a new interestingness measure for mining association rules is proposed based on sufficiency measure of uncertain reasoning to improve the classical method of mining association rules. The property of the new interestingness measures is analyzed, which validity has been tested in this paper. © 2008 IEEE.

Publication Date

11-24-2008

Publication Title

Proceedings - 2nd International Conference on Genetic and Evolutionary Computing, WGEC 2008

Number of Pages

393-397

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/WGEC.2008.34

Socpus ID

56349123087 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/56349123087

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