An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi Ag
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Model-Based Clustering, Cluster Analysis, Information Criteria, Analytic Hierarchy Process
Kaynak
Entropy
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
19
Sayı
9
