A Method to Improve the Analysis of Cluster Ensembles
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 disagreeme...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/31392 |
| Acceso en línea: | http://hdl.handle.net/11336/31392 |
| Access Level: | acceso abierto |
| Palabra clave: | Consensus Clustering Ensemble Diversity Cluster Ensemble Generation https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | 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. |
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