Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Feature weighted kernel clustering with application to medical data analysis

Language

English

Abstract

Clustering technique is an effective tool for medical data analysis as it can work for disease prediction, diagnosis record mining, medical image segmentation, and so on. This paper studies the kernel-based clustering method which can conduct nonlinear partition on input patterns and addresses two challenging issues in unsupervised learning environment: feature relevance estimate and cluster number selection. Specifically, a kernel-based competitive learning paradigm is presented for nonlinear clustering analysis. To distinguish the relevance of different features, a weight variable is associated with each feature to quantify the feature's contribution to the whole cluster structure. Subsequently, the feature weights and cluster assignment are updated alternately during the learning process so that the relevance of features and cluster membership can be jointly optimized. Moreover, to solve the problem of cluster number selection, the cooperation mechanism is further introduced into the presented learning framework and a new kernel clustering algorithm which can automatically select the most appropriate cluster number is educed. The performance of proposed method is demonstrated by the experiments on different medical data sets. © Springer International Publishing 2013.

Keywords

Competitive learning, Cooperation mechanism, Feature weight, Kernel-based clustering, Number of clusters

Publication Date

2013

Source Publication Title

Brain and Health Informatics: International Conference, BHI 2013, Maebashi, Japan, October 29-31, 2013. Proceedings

Start Page

496

End Page

505

Conference Location

Maebashi, Japan

Publisher

Springer International Publishing

DOI

10.1007/978-3-319-02753-1_50

ISBN (print)

9783319027524

ISBN (electronic)

9783319027531

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