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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
entropy-19-00452.pdf | 1.72 MB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
114
checked on Feb 24, 2024
WEB OF SCIENCETM
Citations
120
checked on Nov 21, 2024
Page view(s)
26
checked on Aug 24, 2024
Download(s)
12
checked on Aug 24, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.