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Open AccessArticle

An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis

1
Department of Statistics, Yildiz Technical University, 34220 Istanbul, Turkey
2
Department of Statistics, Necmettin Erbakan University, 42090 Konya, Turkey
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(9), 452; https://doi.org/10.3390/e19090452
Received: 12 July 2017 / Revised: 27 August 2017 / Accepted: 27 August 2017 / Published: 29 August 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
To determine the number of clusters in the clustering analysis that has a broad range of applied sciences, such as physics, chemistry, biology, engineering, economics etc., many methods have been proposed in the literature. The aim of this paper is to determine the number of clusters of a dataset in a model-based clustering by using an Analytic Hierarchy Process (AHP). In this study, the AHP model has been created by using the information criteria Akaike’s Information Criterion (AIC), Approximate Weight of Evidence (AWE), Bayesian Information Criterion (BIC), Classification Likelihood Criterion (CLC), and Kullback Information Criterion (KIC). The achievement of the proposed approach has been tested on common real and synthetic datasets. The proposed approach based on the corresponding information criteria has produced accurate results. The currently produced results have been seen to be more accurate than those corresponding to the information criteria. View Full-Text
Keywords: model-based clustering; cluster analysis; information criteria; analytic hierarchy process model-based clustering; cluster analysis; information criteria; analytic hierarchy process
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Akogul, S.; Erisoglu, M. An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis. Entropy 2017, 19, 452.

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