Clustering the objective interestingness measures based on tendency of variation in statistical implications

Authors

  • Nghia Quoc Phan Travinh University
  • Vinh Cong Phan Trường ĐH Nguyễn Tất Thành image/svg+xml
  • Hung Huu Huynh Danang University of Science and Technology
  • Hiep Xuan Huynh Cantho University

DOI:

https://doi.org/10.4108/eai.2-5-2016.151212

Keywords:

objective interestingness measures, tendency of variation in statistical implications, distance matrix, Similarity tree, Clustering objective interestingness measures

Abstract

In recent years, the research cluster of objective interestingness measures has rapidly developed in order to assist users to choose the appropriate measure for their application. Researchers in this field mainly focus on three main directions: clustering based on the properties of the measures, clustering based on the behavior of measures and clustering tendency of variation in statistical implications. In this paper we propose a new approach to cluster the objective interestingness measures based on tendency of variation in statistical implications. In this proposal, we built the statistical implication data of 31 objective interestingness measures based on the examination of the partial derivatives on four parameters. From this data, two distance matrices of interestingness measures are established based on Euclidean and Manhattan distance. The similarity trees are built based on distance matrix that gave results of 31 measures clustering with two different clustering thresholds.

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Published

02-05-2016

How to Cite

1.
Quoc Phan N, Cong Phan V, Huu Huynh H, Xuan Huynh H. Clustering the objective interestingness measures based on tendency of variation in statistical implications. EAI Endorsed Trans Context Aware Syst App [Internet]. 2016 May 2 [cited 2024 Apr. 20];3(9):e5. Available from: https://publications.eai.eu/index.php/casa/article/view/1984

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