Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Cardoso, M. & Carvalho, A. P. (2009). Quality indices for (practical) clustering evaluation. Intelligent Data Analysis. 13 (5), 725-740
Exportar Referência (IEEE) 
M. M. Cardoso and A. P. Carvalho,  "Quality indices for (practical) clustering evaluation", in Intelligent Data Analysis, vol. 13, no. 5, pp. 725-740, 2009
Exportar BibTeX 
@article{cardoso2009_1732203046207,
	author = "Cardoso, M. and Carvalho, A. P.",
	title = "Quality indices for (practical) clustering evaluation",
	journal = "Intelligent Data Analysis",
	year = "2009",
	volume = "13",
	number = "5",
	doi = "10.3233/IDA-2009-0390",
	pages = "725-740",
	url = ""
}
Exportar RIS 
TY  - JOUR
TI  - Quality indices for (practical) clustering evaluation
T2  - Intelligent Data Analysis
VL  - 13
IS  - 5
AU  - Cardoso, M.
AU  - Carvalho, A. P.
PY  - 2009
SP  - 725-740
SN  - 1088-467X
DO  - 10.3233/IDA-2009-0390
AB  - Clustering quality or validation indices allow the evaluation of the quality of clustering in order to support the selection of a specific partition or clustering structure in its natural unsupervised environment, where the real solution is unknown or not available. In this paper, we investigate the use of quality indices mostly based on the concepts of clusters' compactness and separation, for the evaluation of clustering results (partitions in particular). This work intends to offer a general perspective regarding the appropriate use of quality indices for the purpose of clustering evaluation. After presenting some commonly used indices, as well as indices recently proposed in the literature, key issues regarding the practical use of quality indices are addressed. A general methodological approach is presented which considers the identification of appropriate indices thresholds. This general approach is compared with the simple use of quality indices for evaluating a clustering solution.
ER  -