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Relational visual cluster validity

Ding, Y. and Harrison, R.F. (2007) Relational visual cluster validity. Pattern Recognition Letters, 28 (15). pp. 2071-2079. ISSN 0167-8655

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Abstract

The assessment of cluster validity plays a very important role in cluster analysis. Most commonly used cluster validity methods are based on statistical hypothesis testing or finding the best clustering scheme by computing a number of different cluster validity indices. A number of visual methods of cluster validity have been produced to display directly the validity of clusters by mapping data into two- or three-dimensional space. However, these methods may lose too much information to correctly estimate the results of clustering algorithms. Although the visual cluster validity (VCV) method of Hathaway and Bezdek can successfully solve this problem, it can only be applied for object data, i.e. feature measurements. There are very few validity methods that can be used to analyze the validity of data where only a similarity or dissimilarity relation exists – relational data. To tackle this problem, this paper presents a relational visual cluster validity (RVCV) method to assess the validity of clustering relational data. This is done by combining the results of the non-Euclidean relational fuzzy c-means (NERFCM) algorithm with a modification of the VCV method to produce a visual representation of cluster validity. RVCV can cluster complete and incomplete relational data and adds to the visual cluster validity theory. Numeric examples using synthetic and real data are presented

Item Type: Article
Copyright, Publisher and Additional Information: © 2007 Elsevier B.V. This is an author produced version of a paper published in Pattern Recognition Letters. Uploaded in accordance with the publisher's self archiving policy.
Keywords: Clustering, Cluster validity, Relational data, Non-Euclidean fuzzy c-means, Visual cluster validity
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 31 Jul 2008 11:30
Last Modified: 08 Feb 2013 16:56
Published Version: http://dx.doi.org/10.1016/j.patrec.2007.06.002
Status: Published
Publisher: Elsevier B.V.
Refereed: Yes
Identification Number: 10.1016/j.patrec.2007.06.002
URI: http://eprints.whiterose.ac.uk/id/eprint/4127

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