Ranking discovered rules from data mining with multiple criteria by data envelopment analysis |
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Authors: | Mu-Chen Chen |
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Affiliation: | aInstitute of Traffic and Transportation, National Chiao Tung University 4F, No. 118, Section 1, Chung Hsiao W. Road, Taipei 10012, Taiwan, ROC |
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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. |
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Keywords: | Data mining Association rules Interestingness Data envelopment analysis Multiple criteria |
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