A variational bayesian approach for unsupervised clustering

Mu Song Chen, Hsuan Fu Wang, Chi Pan Hwang, Tze Yee Ho, Chan Hsiang Hung

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Gaussian Mixture Models are among the most statistically mature methods which are used to make statistical inferences as well as performing unsupervised clustering. Formally, a gaussian mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the data set. In this paper, a probabilistic clustering based on the finite mixture models of the data distribution is suggested. An important issue in the finite mixture model-based clustering approach is to select the number of mixture components of clusters. In this sense, we focus on statistical inference for finite mixture models and illustrate how the variational Bayesian approach can be used to determine a suitable number of components in the case of a mixture of Gaussian distributions.

Original languageEnglish
Title of host publicationLecture Notes in Electrical Engineering
PublisherSpringer Verlag
Pages651-660
Number of pages10
DOIs
Publication statusPublished - 2016

Publication series

NameLecture Notes in Electrical Engineering
Volume375
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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    Chen, M. S., Wang, H. F., Hwang, C. P., Ho, T. Y., & Hung, C. H. (2016). A variational bayesian approach for unsupervised clustering. In Lecture Notes in Electrical Engineering (pp. 651-660). (Lecture Notes in Electrical Engineering; Vol. 375). Springer Verlag. https://doi.org/10.1007/978-981-10-0539-8_63