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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) 
Amorim, M. J. & Cardoso, M. G. M. S. (2015). Comparing clustering solutions: the use of adjusted paired indices. Intelligent Data Analysis. 19 (6), 1275-1296
Exportar Referência (IEEE) 
M. J. Amorim and M. M. Cardoso,  "Comparing clustering solutions: the use of adjusted paired indices", in Intelligent Data Analysis, vol. 19, no. 6, pp. 1275-1296, 2015
Exportar BibTeX 
@article{amorim2015_1734878241674,
	author = "Amorim, M. J. and Cardoso, M. G. M. S.",
	title = "Comparing clustering solutions: the use of adjusted paired indices",
	journal = "Intelligent Data Analysis",
	year = "2015",
	volume = "19",
	number = "6",
	doi = "10.3233/IDA-150782",
	pages = "1275-1296",
	url = "http://content.iospress.com/articles/intelligent-data-analysis/ida782"
}
Exportar RIS 
TY  - JOUR
TI  - Comparing clustering solutions: the use of adjusted paired indices
T2  - Intelligent Data Analysis
VL  - 19
IS  - 6
AU  - Amorim, M. J.
AU  - Cardoso, M. G. M. S.
PY  - 2015
SP  - 1275-1296
SN  - 1088-467X
DO  - 10.3233/IDA-150782
UR  - http://content.iospress.com/articles/intelligent-data-analysis/ida782
AB  - In the present paper we compare clustering solutions using indices of paired agreement. We propose a new method - IADJUST - to correct indices of paired agreement, excluding agreement by chance. This new method overcomes previous limitations known in the literature as it permits the correction of any index. We illustrate its use in external clustering validation, to measure the accordance between clusters and an a priori known structure. The adjusted indices are intended to provide a realistic measure of clustering performance that excludes agreement by chance with ground truth. We use simulated data sets, under a range of scenarios - considering diverse numbers of clusters, clusters overlaps and balances - to discuss the pertinence and the precision of our proposal. Precision is established based on comparisons with the analytical approach for correction specific indices that can be corrected in this way are used for this purpose. The pertinence of the proposed correction is discussed when making a detailed comparison between the performance of two classical clustering approaches, namely Expectation-Maximization (EM) and K-Means (KM) algorithms. Eight indices of paired agreement are studied and new corrected indices are obtained.
ER  -