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This article is part of the supplement: Proceedings of the Fourth Annual MCBIOS Conference. Computational Frontiers in Biomedicine

Open Access Proceedings

SVM clustering

Stephen Winters-Hilt12* and Sam Merat1

Author Affiliations

1 Department of Computer Science, University of New Orleans, LA, 70148, USA

2 Research Institute for Children, Children's Hospital, New Orleans, LA, 70118, USA

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BMC Bioinformatics 2007, 8(Suppl 7):S18  doi:10.1186/1471-2105-8-S7-S18

Published: 1 November 2007

Abstract

Background

Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being considered in a number of different ways.

Results

An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labelled, this is repeated until an initial convergence occurs. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabelled data) then has its' labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labelled data) and is guaranteed to converge in this situation since it converged previously, and now it has fewer data points to carry with mislabelling penalties. This approach appears to limit exposure to the local minima traps that can occur with other approaches. Thus, the algorithm then improves on its weakly convergent result by SVM re-training after each re-labeling on the worst of the misclassified vectors – i.e., those feature vectors with confidence factor values beyond some threshold. The repetition of the above process improves the accuracy, here a measure of separability, until there are no misclassifications. Variations on this type of clustering approach are shown.

Conclusion

Non-parametric SVM-based clustering methods may allow for much improved performance over parametric approaches, particularly if they can be designed to inherit the strengths of their supervised SVM counterparts.