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

Nghia, Phan Quoc and Phan, Vinh Cong and Huynh, Hung Huu and Huynh, Hiep Xuan (2016) Clustering the objective interestingness measures based on tendency of variation in statistical implications. EAI Endorsed Transactions on Context-aware Systems and Applications, 3 (9): e5. ISSN 2409-0026

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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.

Item Type: Article
Uncontrolled Keywords: objective interestingness measures, tendency of variation in statistical implications, distance matrix, Similarity tree, Clustering objective interestingness measures
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 16 Sep 2020 12:36
Last Modified: 16 Sep 2020 12:36
URI: https://eprints.eudl.eu/id/eprint/354

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