Clustering right-skewed data stream via Birnbaum–Saunders mixture models: A flexible approach based on fuzzy clustering algorithm
Despite the widespread use of Gaussian mixture model for clustering datasets, practical applications show that the skewed and leptokurtic mixture models can be considered as promising alternatives. This paper proposes a finite mixture of Birnbaum–Saunders (FM-BS) distributions for analyzing and clus...
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| Published in: | Applied soft computing Vol. 82; p. 105539 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.09.2019
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | Despite the widespread use of Gaussian mixture model for clustering datasets, practical applications show that the skewed and leptokurtic mixture models can be considered as promising alternatives. This paper proposes a finite mixture of Birnbaum–Saunders (FM-BS) distributions for analyzing and clustering right-skewed, leptokurtic, and multimodal lifetime datasets. The maximum likelihood (ML) estimates of the proposed model are obtained by developing a computationally analytical expectation–maximization (EM) type algorithm, as well as a fuzzy classification maximum likelihood (FCML) type algorithm, that combines the advantages of fuzzy clustering and robust statistical estimators. Simulation studies demonstrate the accuracy and computational efficiency of the FCML algorithm to estimate parameters of the FM-BS distributions and to cluster samples drawn from the FM-BS distributions. Finally, some real datasets have been analyzed to illustrate how well the proposed FM-BS model estimates the membership values.
•A finite mixture model for clustering right-skewed, and multimodal data is proposed.•The EM and FCML type algorithms are implemented for computing ML estimates.•Asymptotic standard errors of parameter estimate are obtained through. |
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| AbstractList | Despite the widespread use of Gaussian mixture model for clustering datasets, practical applications show that the skewed and leptokurtic mixture models can be considered as promising alternatives. This paper proposes a finite mixture of Birnbaum–Saunders (FM-BS) distributions for analyzing and clustering right-skewed, leptokurtic, and multimodal lifetime datasets. The maximum likelihood (ML) estimates of the proposed model are obtained by developing a computationally analytical expectation–maximization (EM) type algorithm, as well as a fuzzy classification maximum likelihood (FCML) type algorithm, that combines the advantages of fuzzy clustering and robust statistical estimators. Simulation studies demonstrate the accuracy and computational efficiency of the FCML algorithm to estimate parameters of the FM-BS distributions and to cluster samples drawn from the FM-BS distributions. Finally, some real datasets have been analyzed to illustrate how well the proposed FM-BS model estimates the membership values.
•A finite mixture model for clustering right-skewed, and multimodal data is proposed.•The EM and FCML type algorithms are implemented for computing ML estimates.•Asymptotic standard errors of parameter estimate are obtained through. |
| ArticleNumber | 105539 |
| Author | Mashinchi, Mashallah Naderi, Mehrdad Hashemi, Farzane |
| Author_xml | – sequence: 1 givenname: Farzane surname: Hashemi fullname: Hashemi, Farzane email: farzane.hashemi1367@yahoo.com organization: Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran – sequence: 2 givenname: Mehrdad surname: Naderi fullname: Naderi, Mehrdad email: m.naderi@up.ac.za organization: Department of Statistics, Faculty of Natural & Agricultural Sciences, University of Pretoria, Pretoria, South Africa – sequence: 3 givenname: Mashallah surname: Mashinchi fullname: Mashinchi, Mashallah email: mashinchi@uk.ac.ir organization: Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran |
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| CitedBy_id | crossref_primary_10_1016_j_asoc_2020_106797 crossref_primary_10_1007_s42952_020_00063_8 crossref_primary_10_1016_j_amc_2020_125712 crossref_primary_10_1007_s40314_022_01875_6 crossref_primary_10_1007_s40995_020_01020_0 crossref_primary_10_1007_s40304_021_00260_9 crossref_primary_10_1002_sam_70027 crossref_primary_10_1016_j_amc_2020_125109 crossref_primary_10_1016_j_cam_2023_115433 |
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