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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Bibliographic Details
Published in:Pattern recognition Vol. 45; no. 11; pp. 3950 - 3961
Main Authors: Yang, Miin-Shen, Lai, Chien-Yo, Lin, Chih-Ying
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.11.2012
Elsevier
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2012.04.031