A new feature weighted fuzzy C-means clustering algorithm

Fu, Huaiguo and Elmisery, Ahmed M. (2009) A new feature weighted fuzzy C-means clustering algorithm. In: Proceedings of the IADIS European Conference on Data Mining 2009, ECDM'09 Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009 :. Proceedings of the IADIS European Conference on Data Mining 2009, ECDM'09 Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009 . UNSPECIFIED, PRT, pp. 11-18. ISBN 9789728924881

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Abstract

In the field of cluster analysis, most of existing algorithms assume that each feature of the samples plays a uniform contribution for cluster analysis. Feature-weight assignment is a special case of feature selection where different features are ranked according to their importance. The feature is assigned a value in the interval [0, 1] indicating the importance of that feature, we call this value "feature-weight". In this paper we propose a new feature weighted fuzzy c-means clustering algorithm in a way which this algorithm be able to obtain the importance of each feature, and then use it in appropriate assignment of feature-weight. These weights incorporated into the distance measure to shape clusters based on variability, correlation and weighted features.

Item Type:	Book Section
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1700/1702
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:17
Last Modified:	04 Feb 2023 00:03
URI:	http://repository-testing.wit.ie/id/eprint/5080

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