Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
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DOI：
10.1016/j.eswa.2006.08.007
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发表时间：
2007-11-01
影响因子：
8.5
通讯作者：
Chen, Mu-Chen
中科院分区：
文献类型：
--
作者：
Chen, Mu-Chen
在数据挖掘应用中，开发用于选择质量和盈利规则的评估方法是很重要的。利用一种非参数方法--数据包络分析(DEA)，对多准则关联规则的效率进行了估计和排序。关联规则的兴趣度通常是基于支持度和置信度来衡量的。对于特定的应用，领域知识可以进一步设计为评估发现的规则的措施。例如，在市场篮子分析中，与关联规则关联的产品价值和交叉销售利润可以作为规则兴趣度的基本衡量标准。在本文中，这些领域度量也被包括在规则排名过程中，以选择有价值的规则来实现。以市场篮子分析为例，说明了基于数据包络分析的多准则关联规则效率度量方法。(C)2006爱思唯尔有限公司。保留所有权利。
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.