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Performance comparison of particle swarm optimization with traditional clustering algorithms used in self organizing map

Sharma, Anuraganand and Omlin, Christian W. (2009) Performance comparison of particle swarm optimization with traditional clustering algorithms used in self organizing map. International Journal of Computational Intelligence, 5 (1.1). pp. 1-12. ISSN 1304-4508

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Abstract

Self-organizing map (SOM) is a well known data reduction technique used in data mining. It can reveal structure in data sets through data visualization that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very tedious. There are several techniques for the automatic detection of clusters of code vectors found by SOM, but they generally do not take into account the distribution of code vectors; this may lead to unsatisfactory clustering and poor definition of cluster boundaries, particularly where the density of data points is low. In this paper, we propose the use of an adaptive heuristic particle swarm optimization (PSO) algorithm for finding cluster boundaries directly from the code vectors obtained from SOM. The application of our method to several standard data sets demonstrates its feasibility. PSO algorithm utilizes a so-called U-matrix of SOM to determine cluster boundaries; the results of this novel automatic method compare very favorably to boundary detection through traditional algorithms namely k-means and hierarchical based approach which are normally used to interpret the output of SOM.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: Ms Neha Harakh
Date Deposited: 18 Oct 2009 12:45
Last Modified: 07 Oct 2013 15:55
URI: http://repository.usp.ac.fj/id/eprint/246
UNSPECIFIED

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