Horta, Danilo, and Campello, Ricardo J.G.B. (2010) Evolutionary clustering of relational data. International Journal of Hybrid Intelligent Systems, 7 (4). pp. 261-281.

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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 (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of clusters in relational data. The computational complexities of the algorithms are discussed and an extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed.

Item ID:	47674
Item Type:	Article (Research - C1)
ISSN:	1875-8819
Keywords:	evolutionary algorithms, relational data clustering
Funders:	CNPq, Brazil, FAPESP
Date Deposited:	23 May 2017 02:01
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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