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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 268-270Metric-Based Semi-Supervised Fuzzy C-Means...

Metric-Based Semi-Supervised Fuzzy C-Means Clustering

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Abstract:

This paper presents a metric-based semi-supervised fuzzy c-means algorithm called MSFCM. Through using side information and unlabeled data together, MSFCM can be applied to both clustering and classification tasks. The resulting algorithm has the following advantages compared with semi-supervised clustering: firstly, membership degree as side information is used to guide the clustering of the data; secondly, through the metric learned, clustering accuracy can be greatly improved. Experimental results on a collection of real-world data sets demonstrated the effectiveness of the proposed algorithm.

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Computational Materials Science

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Periodical:

Advanced Materials Research (Volumes 268-270)

Pages:

166-171

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.268-270.166

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Online since:

July 2011

Authors:

Xue Song Yin, Qi Huang, Liang Ming Li

Keywords:

Fuzzy C-Means (FCM), Membership Degree, Semi-Supervised Clustering, Side Information

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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