A Liouville-based approach for discrete data categorization
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
EDA-USL: unsupervised clustering algorithm based on estimation of distribution algorithm
International Journal of Wireless and Mobile Computing
Statistical methods for data mining and knowledge discovery
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
Expert Systems with Applications: An International Journal
On online high-dimensional spherical data clustering and feature selection
Engineering Applications of Artificial Intelligence
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In this paper, we consider the problem of unsupervised discrete feature selection/weighting. Indeed, discrete data are an important component in many data mining, machine learning, image processing, and computer vision applications. However, much of the published work on unsupervised feature selection has concentrated on continuous data. We propose a probabilistic approach that assigns relevance weights to discrete features that are considered as random variables modeled by finite discrete mixtures. The choice of finite mixture models is justified by its flexibility which has led to its widespread application in different domains. For the learning of the model, we consider both Bayesian and information-theoretic approaches through stochastic complexity. Experimental results are presented to illustrate the feasibility and merits of our approach on a difficult problem which is clustering and recognizing visual concepts in different image data. The proposed approach is successfully applied also for text clustering.