Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Finding overlapping components with MML
Statistics and Computing
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Novel mixtures based on the dirichlet distribution: application to data and image classification
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
IEEE Transactions on Image Processing
GUEST EDITORIAL: Intelligent data analysis in medicine-Recent advances
Artificial Intelligence in Medicine
Online clustering via finite mixtures of Dirichlet and minimum message length
Engineering Applications of Artificial Intelligence
Positive vectors clustering using inverted Dirichlet finite mixture models
Expert Systems with Applications: An International Journal
An infinite mixture of inverted dirichlet distributions
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Spatial color image segmentation based on finite non-Gaussian mixture models
Expert Systems with Applications: An International Journal
Infinite Dirichlet mixture models learning via expectation propagation
Advances in Data Analysis and Classification
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This paper proposes an unsupervised algorithm for learning a finite Dirichlet mixture model. An important part of the unsupervised learning problem is determining the number of clusters which best describe the data. We consider here the application of the Minimum Message length (MML) principle to determine the number of clusters. The Model is compared with results obtained by other selection criteria (AIC, MDL, MMDL, PC and a Bayesian method). The proposed method is validated by synthetic data and summarization of texture image database.