Clustering the objective interestingness measures based on tendency of variation in statistical implications
DOI:
https://doi.org/10.4108/eai.2-5-2016.151212Keywords:
objective interestingness measures, tendency of variation in statistical implications, distance matrix, Similarity tree, Clustering objective interestingness measuresAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 EAI Endorsed Transactions on Context-aware Systems and Applications
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.