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

被引：0

作者：

Somyanonthanakul, Rachasak ^{[1
]}

Roonsamrarn, Monnapat ^{[2
]}

Theeramunkong, Thanaruk ^{[1
]}

机构：

[1] Thammasat Univ, SIIT, Sch ICT, Pathum Thani, Thailand

[2] Panyapiwat Inst Management, Fac Management Sci, Nonthaburi, Thailand

来源：

2018 INTERNATIONAL JOINT SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE PROCESSING (ISAI-NLP 2018) | 2018年

关键词：

Similarity measures; Association rules; Data mining;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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(AB), P(AB), P(AB), P(A) and P(B) 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.

引用

页码：213 / 220

页数：8

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