Clustering right-skewed data stream via Birnbaum–Saunders mixture models: A flexible approach based on fuzzy clustering algorithm

Farzane Hashemi, Mehrdad Naderi*, Mashallah Mashinchi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Article number105539
Number of pages10
JournalApplied Soft Computing
Early online date3 Jun 2019
Publication statusPublished - 1 Sept 2019
Externally publishedYes

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