NIVA: a Robust cluster validity

Authors:
Erendira Rendón;Rene Garcia;Itzel Abundez;Citlalih Gutierrez;Eduardo Gasca;Federico Del Razo;Adrian Gonzalez
Affiliations:
División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Toluca, Metepec, Edo. de México, México;División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Toluca, Metepec, Edo. de México, México;División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Toluca, Metepec, Edo. de México, México;División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Toluca, Metepec, Edo. de México, México;División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Toluca, Metepec, Edo. de México, México;División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Toluca, Metepec, Edo. de México, México;División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Toluca, Metepec, Edo. de México, México
Venue:
ICCOM'08 Proceedings of the 12th WSEAS international conference on Communications
Year:
2008

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Abstract

Clustering aims at extracting hidden structures in datasets. Many validity indices have been proposed to evaluate clustering results; some of them work well when clusters have different densities and sizes and others with different shapes. They usually have a tendency to consider one or two characteristics simultaneously. In this paper, we present a cluster validity index that takes advantage of the density, size and shape of cluster characteristics. The proposed index is experimentally compared with PS, CS and S_Dbw indices using 12 synthetic datasets. Our proposed index improves others indices.