Adaptive radius immune algorithm for data clustering

Authors:
George B. Bezerra;Tiago V. Barra;Leandro N. de Castro;Fernando J. Von Zuben
Affiliations:
Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC), Department of Computer Engineering and Industrial Automation, University of Campinas, Unicamp, Campinas, SP, Brazil;Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC), Department of Computer Engineering and Industrial Automation, University of Campinas, Unicamp, Campinas, SP, Brazil;Graduate Program on Informatics, Catholic University of Santos, COPOP, Santos, SP, Brazil;Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC), Department of Computer Engineering and Industrial Automation, University of Campinas, Unicamp, Campinas, SP, Brazil
Venue:
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Year:
2005

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Abstract

Many algorithms perform data clustering by compressing the original data into a more compact and interpretable representation, which can be more easily inspected for the presence of clusters. This, however, can be a risky alternative, because the simplified representation may contain distortions mainly related to the density information present in the data, which can considerably act on the clustering results. In order to treat this deficiency, this paper proposes an Adaptive Radius Immune Algorithm (ARIA), which is capable of maximally preserving the density information after compression by implementing an antibody adaptive suppression radius that varies inversely with the local density in the space. ARIA is tested with both artificial and real world problems obtaining a better performance than the aiNet algorithm and showing that preserving the density information leads to refined clustering results.