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Annals of Data Science
Published in: Annals of Data Science 1/2014

01-03-2014

Proposal of New Objective Measures for Mining Association Rules: Cannibalization and Unexpectedness

Authors: Hidenobu Hashikami, Masato Koda

Published in: Annals of Data Science | Issue 1/2014

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Abstract

In view of the problems that a few of the existing measures for association rules does not directly meet user’s requirements, and association mining algorithms produce huge number of trivial rules, this paper proposes two new objective measures for mining association rules to solve the problems. The first measure is the degree of cannibalization between itemsets, which is bounded up with marketing strategy, and the second is the objective measure that intends to discover unexpected rules in the database. Experimental studies with application to public dataset and comparison of running time using synthetic datasets demonstrate the validity and effectiveness of the proposed measures.
previous article Exact Solutions to the Capacitated Clustering Problem: A Comparison of Two Models
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Metadata
Title
Proposal of New Objective Measures for Mining Association Rules: Cannibalization and Unexpectedness
Authors
Hidenobu Hashikami
Masato Koda
Publication date
01-03-2014
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 1/2014
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-014-0011-y

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