Unsupervised non parametric data clustering by means of Bayesian inference and information theory

G. Bougeniere, C. Cariou, K. Chehdi, Alan Gay

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (SciVal)


In this communication, we propose a novel approach to perform the unsupervised and non parametric clustering of n-D data upon a Bayesian framework. The iterative approach developed is derived from the Classification Expectation-Maximization (CEM) algorithm, in which the parametric modelling of the mixture density is replaced by a non parametric modelling using local kernels, and the posterior probabilities account for the coherence of current clusters through the measure of class-conditional entropies. Applications of this method to synthetic and real data including multispectral images are presented. The classification issues are compared with other recent unsupervised approaches, and we show that our method reaches a more reliable estimation of the number of clusters while providing slightly better rates of correct classification in average.
Original languageEnglish
Title of host publicationProceedings of the International Conference on signal processing and multimedia applications (SIGMAP)
PublisherInstitute for Systems and Technologies of Information, Control and Communication Press
Number of pages8
ISBN (Print)9789898111135
Publication statusPublished - 2007


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