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Soft ranking in clustering

Rovetta, S., Masulli, F. and Filippone, M. (2009) Soft ranking in clustering. Neurocomputing, 72 (7-9). pp. 2028-2031. ISSN 0925-2312

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Abstract

Due to the diffusion of large-dimensional data sets (e.g., in DNA microarray or document organization and retrieval applications), there is a growing interest in clustering methods based on a proximity matrix. These have the advantage of being based on a data structure whose size only depends on cardinality, not dimensionality. In this paper, we propose a clustering technique based on fuzzy ranks. The use of ranks helps to overcome several issues of large-dimensional data sets, whereas the fuzzy formulation is useful in encoding the information contained in the smallest entries of the proximity matrix. Comparative experiments are presented, using several standard hierarchical clustering techniques as a reference.

Item Type: Article
Copyright, Publisher and Additional Information: © 2009 Elsevier. This is an author produced version of a paper subsequently published in Neurocomputing. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Fuzzy rank; clustering; data mining; DNA Microarrays
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Miss Anthea Tucker
Date Deposited: 28 Apr 2009 15:21
Last Modified: 08 Feb 2013 16:58
Published Version: http://dx.doi.org/10.1016/j.neucom.2008.11.015
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.neucom.2008.11.015
URI: http://eprints.whiterose.ac.uk/id/eprint/8534

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