An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis
An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis
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DOI：
10.3390/e19090452
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发表时间：
2017-09-01
期刊：
影响因子：
2.7
通讯作者：
Erisoglu, Murat
中科院分区：
文献类型：
--
作者：
Akogul, Serkan;Erisoglu, Murat
聚类分析在物理、化学、生物学、工程学、经济学等应用科学领域有着广泛的应用，为了确定聚类的数量，文献中提出了许多方法。本文的目的是利用层次分析法（AHP）确定基于模型的聚类中数据集的聚类数量。本研究采用Akaike信息准则（AIC）、近似证据权（AWE）、贝叶斯信息准则（BIC）、分类似然准则（CLC）和Kullback信息准则（KIC）等信息准则建立了AHP模型。在常见的真实数据集和合成数据集上对该方法的效果进行了测试。该方法基于相应的信息标准，产生了准确的结果。目前产生的结果已被认为比符合资料标准的结果更为准确。
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.