Artículo
A Method to Improve the Analysis of Cluster Ensembles
Fecha de publicación:
03/2014
Editorial:
Sociedad Iberoamericana de Inteligencia Artificial
Revista:
Inteligencia Artificial
ISSN:
1137-3601
e-ISSN:
1988-3064
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Clustering is fundamental to understand the structure of data. In the past decade the cluster ensembleproblem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a singleconsensus solution that outperforms all the ensemble members. However, there is disagreement about which arethe best ensemble characteristics to obtain a good performance: some authors have suggested that highly differentpartitions within the ensemble are beneï¬ cial for the ï¬ nal performance, whereas others have stated that mediumdiversity among them is better. While there are several measures to quantify the diversity, a better method toanalyze the best ensemble characteristics is necessary. This paper introduces a new ensemble generation strategyand a method to make slight changes in its structure. Experimental results on six datasets suggest that this isan important step towards a more systematic approach to analyze the impact of the ensemble characteristics onthe overall consensus performance.
Palabras clave:
Consensus Clustering
,
Ensemble Diversity
,
Cluster Ensemble Generation
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(CCT - SANTA FE)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Articulos(SINC(I))
Articulos de INST. DE INVESTIGACION EN SEÑALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL
Articulos de INST. DE INVESTIGACION EN SEÑALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL
Citación
Milone, Diego Humberto; Stegmayer, Georgina; Pividori, Milton Damián; A Method to Improve the Analysis of Cluster Ensembles; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 17; 53; 3-2014; 46-56
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