A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method

Determining the number of clusters in a data set is a significant and difficult problem in cluster analysis. In this study, a new model-based clustering approach is proposed for the estimation of the number of clusters. In the proposed method, the number of components in each variable is determined...

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Bibliographic Details
Published in:	IEEE access Vol. 9; pp. 159987 - 160003
Main Author:	Gogebakan, Maruf
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	appropriate Gaussian mixture models Arrays Cluster analysis Clustering Clustering algorithms components of heterogeneous variable Computational modeling Data models Datasets Estimation Gaussian mixture model Image classification information criteria Landsat satellites mixture model soft computing method Mixture models Model-based clustering Parameter estimation Probabilistic models Satellite imagery Soft computing Statistics variable selection
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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