Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/4212
Title: Clustering the objective interestingness measures based on tendency of variation in statistical implications
Authors: Phan, Quốc Nghĩa
Huỳnh, Xuân Hiệp
Huỳnh, Hữu Hưng
Phan, Công Vinh
Keywords: Objective interestingness measures
Tendency of variation in statistical implications
Distance matrix
Similarity tree
Clustering objective interestingness measures
Issue Date: 2016
Series/Report no.: EAI Endorsed Transactions on Context-aware Systems and Applications;16 .- p.1-8
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.
URI: http://dspace.ctu.edu.vn/jspui/handle/123456789/4212
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