Feature selection and semisupervised fuzzy clustering

Yi-qing Kong () and Shi-tong Wang
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Yi-qing Kong: Jiangnan University
Shi-tong Wang: Jiangnan University

Fuzzy Information and Engineering, 2009, vol. 1, issue 2, 179-190

Abstract: Abstract Semisupervised fuzzy clustering plays an important role in discovering structure in data set with both labelled and unlabelled data. The proposed method learns the task of classification and feature selection through the generalized form of Fuzzy C-means. Experimental results illustrate appropriate feature selection and classification accuracy with both synthetic and benchmark data sets.

Keywords: Semisupervised clustering; Feature selection (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/s12543-009-0014-0

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