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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Veröffentlicht in:	Pattern recognition Jg. 45; H. 11; S. 3950 - 3961
Hauptverfasser:	Yang, Miin-Shen, Lai, Chien-Yo, Lin, Chih-Ying
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.11.2012 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
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