A robust EM clustering algorithm for Gaussian mixture models

Authors:
Miin-Shen Yang;Chien-Yo Lai;Chih-Ying Lin
Affiliations:
Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan
Venue:
Pattern Recognition
Year:
2012

Citing 16
Cited 3

Algorithms for clustering data

Algorithms for clustering data
Deterministic annealing EM algorithm

Neural Networks
Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Robust mixture modelling using the t distribution

Statistics and Computing
Mean Shift, Mode Seeking, and Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models

Computational Statistics & Data Analysis
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures

Neural Computation
Mean shift-based clustering

Pattern Recognition
On convergence properties of the em algorithm for gaussian mixtures

Neural Computation
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust mixture modeling based on scale mixtures of skew-normal distributions

Computational Statistics & Data Analysis
Robust mixture clustering using Pearson type VII distribution

Pattern Recognition Letters
Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution

Statistics and Computing
Hierarchical unsupervised fuzzy clustering

IEEE Transactions on Fuzzy Systems
Quantization and the method of -means

IEEE Transactions on Information Theory

Clustering construction on a multimodal probability model

Information Sciences: an International Journal
NetCluster: A clustering-based framework to analyze internet passive measurements data

Computer Networks: The International Journal of Computer and Telecommunications Networking
Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions

Applied Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.