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Semantic-based Relationship between Objective Interestingness Measures in Association Rules Mining

机译:关联规则挖掘中目标兴趣度量之间基于语义的关系

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This work investigates the semantic of 61 commonly used interestingness measures in order to explore their common and distinct characteristics, by means of a two-way contingency table of a pair of variables; A and B. As the first step, a synthetic data of six probability variables; P(AB), P(AB̅), P(A̅B),P(A̅B̅), P(A̅) and P(B̅) and profile of measurements are generated based on P(A), P(B), and P(AB). The exploration will be done based on semantic relationship. Secondly, an extension is done to characterize among 61 interestingness measures. Thirdly, their similarity and dissimilarity among the measurments are investigated in terms of association and correlation points of view. Finally, the semantic hidden in the properties of each measure is revealed.
机译:这项工作通过使用一对变量的双向列联表,调查了61种常用趣味性测度的语义,以探究它们的共同特征。 A和B。第一步,是六个概率变量的综合数据。 P(AB),P(AB̅),P(A̅B),P(A̅B̅),P(A̅)和P(B̅),并根据P(A),P(B)和P( AB)。探索将基于语义关系进行。其次,进行了扩展以表征61个兴趣度指标。第三,从关联和关联的角度研究了它们在度量之间的相似性和异同性。最后,揭示了每个度量的属性中隐藏的语义。

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