Discrete data clustering using finite mixture models

Authors:
Nizar Bouguila;Walid ElGuebaly
Affiliations:
Faculty of Engineering and Computer Science, Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Que., Canada H3G 2W1;Faculty of Engineering and Computer Science, Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Que., Canada H3G 2W1
Venue:
Pattern Recognition
Year:
2009

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Abstract

Finite mixture models have been applied for different computer vision, image processing and pattern recognition tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this paper, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel, well motivated mixture that we call the multinomial generalized Dirichlet mixture. The novel model is compared with other discrete mixtures. We designed experiments involving spatial color image databases modeling and summarization, and text classification to show the robustness, flexibility and merits of our approach.