Analyzing Behavior of Objective Rule Evaluation Indices Based on Pearson Product-Moment Correlation Coefficient
Analyzing Behavior of Objective Rule Evaluation Indices Based on Pearson Product-Moment Correlation Coefficient
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DOI：
10.1007/978-3-540-68123-6_9
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
H. Abe;S. Tsumoto
中科院分区：
文献类型：
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作者：
H. Abe;S. Tsumoto
在本文中，我们提出了一个行为的客观规则评价指标的分类规则集上使用皮尔逊积矩相关系数之间的每个指标。数据挖掘后处理是数据挖掘过程中的一个重要环节，为了支持后处理，提出了至少40个指标来发现有价值的知识。然而，他们的行为从来没有被清楚地阐明。因此，我们对各个客观规则评价指标之间进行了相关性分析。在这个分析中，我们使用bootstrap方法对32个用信息增益比学习的分类规则集计算每个指标的平均值。然后，我们发现以下关系的相关系数值的基础上：相似的对，差异对，和独立的指数。针对这一结果，我们讨论了各组客观指标之间的相对函数关系。
In this paper, we present an analysis of behavior of objective rule evaluation indices on classification rule sets using Pearson product-moment correlation coefficients between each index. To support data mining post-processing, which is one of important procedures in a data mining process, at least 40 indices are proposed to find out valuable knowledge. However, their behavior have never been clearly articulated. Therefore, we carried out a correlation analysis between each objective rule evaluation indices. In this analysis, we calculated average values of each index using bootstrap method on 32 classification rule sets learned with information gain ratio. Then, we found the following relationships based on the correlation coefficient values: similar pairs, discrepant pairs, and independent indices. With regarding to this result, we discuss about relative functional relationships between each group of objective indices.