Title: Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Authors: Chen, Mu-Chen
運輸與物流管理系 註:原交通所+運管所
Department of Transportation and Logistics Management
Keywords: data mining;association rules;interestingness;data envelopment analysis;multiple criteria
Issue Date: 1-Nov-2007
Abstract: In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules. This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria. The interestingness of association rules is conventionally measured based on support and confidence. For specific applications, domain knowledge can be further designed as measures to evaluate the discovered rules. For example, in market basket analysis, the product value and cross-selling profit associated with the association rule can serve as essential measures to rule interestingness. In this paper, these domain measures are also included in the rule ranking procedure for selecting valuable rules for implementation. An example of market basket analysis is applied to illustrate the DEA based methodology for measuring the efficiency of association rules with multiple criteria. (c) 2006 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2006.08.007
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2006.08.007
Volume: 33
Issue: 4
Begin Page: 1110
End Page: 1116
Appears in Collections:Articles

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