Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/25842
Title: An approach for determining the number of clusters in a model-based cluster analysis
Authors: Akogul, Serkan
Erisoglu, Murat
Keywords: model-based clustering
information criteria
cluster analysis
analytic hierarchy process
Abstract: 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 produc
URI: https://hdl.handle.net/11499/25842
https://doi.org/10.3390/e19090452
ISSN: 1099-4300
Appears in Collections:Fen-Edebiyat Fakültesi Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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