A robust EM clustering algorithm for Gaussian mixture models

Clustering is a useful tool for finding structure in a data set. The mixture likelihood approach to clustering is a popular clustering method, in which the EM algorithm is the most used method. However, the EM algorithm for Gaussian mixture models is quite sensitive to initial values and the number...

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Vydáno v:	Pattern recognition Ročník 45; číslo 11; s. 3950 - 3961
Hlavní autoři:	Yang, Miin-Shen, Lai, Chien-Yo, Lin, Chih-Ying
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.11.2012 Elsevier
Témata:	Algorithms Applied sciences Cluster analysis Clustering Clusters Construction EM algorithm Exact sciences and technology Gaussian Gaussian mixture model Information, signal and communications theory Initialization Number of clusters Optimization Pattern recognition Robust EM Signal and communications theory Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Cluster analysis Initialization Number of clusters EM algorithm Robust EM Gaussian mixture model Automatic classification Gaussian process Volume A priori estimation Mixture theory Signal classification
ISSN:	0031-3203, 1873-5142
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Clustering is a useful tool for finding structure in a data set. The mixture likelihood approach to clustering is a popular clustering method, in which the EM algorithm is the most used method. However, the EM algorithm for Gaussian mixture models is quite sensitive to initial values and the number of its components needs to be given a priori. To resolve these drawbacks of the EM, we develop a robust EM clustering algorithm for Gaussian mixture models, first creating a new way to solve these initialization problems. We then construct a schema to automatically obtain an optimal number of clusters. Therefore, the proposed robust EM algorithm is robust to initialization and also different cluster volumes with automatically obtaining an optimal number of clusters. Some experimental examples are used to compare our robust EM algorithm with existing clustering methods. The results demonstrate the superiority and usefulness of our proposed method. ► We propose a robust EM clustering algorithm for Gaussian mixture models. ► We create a new way to solve these initialization problems of the EM algorithm. ► We construct a schema to automatically obtain an optimal number of clusters. ► The proposed robust EM algorithm is robust to initialization, cluster number, and different cluster volumes.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0031-3203 1873-5142
DOI:	10.1016/j.patcog.2012.04.031