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Dynamic Relation-Based Analysis of Objective Interestingness Measures in Association Rules Mining

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Advances in Intelligent Informatics, Smart Technology and Natural Language Processing (iSAI-NLP 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 807))

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Abstract

While a large number of objective interestingness measures have been proposed to extract interesting rules from a dataset, most of them have been tested on a limited number of datasets that may not cover all possible patterns. This paper presents a framework to investigate relation among twenty-one interestingness measures on synthesized patterns (A → B), using all combinations of the six probabilities P(A, B), P(A, ¬B), P(¬A, B), P(¬A, ¬B), P(A) and, P(B) with a fixed number of occurrences. The partial order of interestingness measures is compared to that of another measure in order to characterize their similarity. The result shows 75 interrelation patterns of probabilities. An association rule mining is used to analyzed to describe for understanding their common and distinct properties.

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Acknowledgements

This work has been supported by Sirindhorn International Institute of Technology, Thammasat University.

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Authors and Affiliations

  1. School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand

    Rachasak Somyanonthanakul & Thannaruk Theeramunkong

  2. The Royal Society of Thailand, Bangkok, Thailand

    Thannaruk Theeramunkong

Authors
  1. Rachasak Somyanonthanakul
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  2. Thannaruk Theeramunkong
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Corresponding author

Correspondence to Rachasak Somyanonthanakul .

Editor information

Editors and Affiliations

  1. Sirindhorn International Institute of Technology (SIIT), Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  2. Faculty of Science and Technology, Department of Computer Science, Thammasat University, Pathumthani, Thailand

    Rachada Kongkachandra

  3. Department of Information Technology Management, Faculty of Information Technology , King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok, Thailand

    Mahasak Ketcham

  4. Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakorn Pathom, Thailand

    Narit Hnoohom

  5. Faculty of Science and Technology, Department of Computer Science, Thammasat University , Pathum Thani, Thailand

    Pokpong Songmuang

  6. National Science and Technology Development Agency, Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Pathum Thani, Thailand

    Thepchai Supnithi

  7. Prince of Songkla University , Phuket, Thailand

    Kiyota Hashimoto

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Somyanonthanakul, R., Theeramunkong, T. (2019). Dynamic Relation-Based Analysis of Objective Interestingness Measures in Association Rules Mining. In: Theeramunkong, T., et al. Advances in Intelligent Informatics, Smart Technology and Natural Language Processing. iSAI-NLP 2017. Advances in Intelligent Systems and Computing, vol 807. Springer, Cham. https://doi.org/10.1007/978-3-319-94703-7_4

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