Fast evolutionary algorithms for relational clustering

Horta, Danilo, and Campello, Ricardo J.G.B. (2009) Fast evolutionary algorithms for relational clustering. In: Proceedings of the 9th International Conference on Intelligent Systems Design and Applications, pp. 1456-1462. From: ISDA 2009: 9th International Conference on Intelligent Systems Design and Applications, 30 November - 2 December 2009, Pisa, Italy.

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Abstract

This paper is concerned with the computational efficiency of clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. Two relational versions of an evolutionary algorithm for clustering are derived and compared against two systematic (repetitive) approaches that can also be used to automatically estimate the number of clusters in relational data. Exhaustive experiments involving six artificial and two real data sets are reported and analyzed.

Item ID: 47062
Item Type: Conference Item (Research - E1)
ISBN: 978-0-7695-3872-3
Funders: CNPq, FAPESP
Date Deposited: 04 Jan 2017 08:04
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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