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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Bibliographic Details
Published in:Applied soft computing Vol. 82; p. 105539
Main Authors: Hashemi, Farzane, Naderi, Mehrdad, Mashinchi, Mashallah
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2019
Subjects:
Classification maximum likelihood
EM-type algorithm
Finite mixture of Birnbaum–Saunders distributions
Fuzzy clustering
Fuzzy clustering
Classification maximum likelihood
Finite mixture of Birnbaum–Saunders distributions
EM-type algorithm
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary: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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2019.105539