CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.01 - January

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Issue No.01 - January (2009 vol.31)

pp: 74-85

Jing Tang , University of Helsinki, Helsinki

Bernard De Baets , Ghent University, Ghent

Peter Dawyndt , Ghent University, Ghent

Jukka Corander , Abo Akademi University, Fanriksgatan

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.53

ABSTRACT

Bayesian model-based classifiers, both unsupervised and supervised, have been studied extensively and their value and versatility have been demonstrated on a wide spectrum of applications within science and engineering. A majority of the classifiers are built on the assumption of intrinsic discreteness of the considered data features or on the discretization of them prior to the modeling. On the other hand, Gaussian mixture classifiers have also been utilized to a large extent for continuous features in the Bayesian framework. Often the primary reason for discretization in the classification context is the simplification of the analytical and numerical properties of the models. However, the discretization can be problematic due to its \textit{ad hoc} nature and the decreased statistical power to detect the correct classes in the resulting procedure. We introduce an unsupervised classification approach for fuzzy feature vectors that utilizes a discrete model structure while preserving the continuous characteristics of data. This is achieved by replacing the ordinary likelihood by a binomial quasi-likelihood to yield an analytical expression for the posterior probability of a given clustering solution. The resulting model can be justified from an information-theoretic perspective. Our method is shown to yield highly accurate clusterings for challenging synthetic and empirical data sets.

INDEX TERMS

Bayesian clustering, quasi-likelihood, fuzzy modeling, continuous data

CITATION

Jing Tang, Bernard De Baets, Peter Dawyndt, Jukka Corander, "Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.31, no. 1, pp. 74-85, January 2009, doi:10.1109/TPAMI.2008.53REFERENCES