Invited talk: To compare clustering partitions, Rand index (RI) and Adjusted Rand index (ARI) are commonly used for measuring the agreement between partitions. Both these external validation indexes aim to analyze how close is a cluster to a reference (or to prior knowledge about the data) by counting corrected classified pairs of elements. When the aim is to evaluate the solution of a fuzzy clustering algorithm, the computation of these measures require converting the soft partitions into hard ones. It is known that different fuzzy partitions describing very different structures in the data can lead to the same crisp partition and consequently to the same values of these measures. We compare the existing approaches to evaluate the external validation criteria in fuzzy clustering and we propose an extension of the ARI for fuzzy partitions based on the normalized degree of concordance, which we call Adjusted Concordance Index. Through use of real and simulated data, we analyze and evaluate the performance of our proposal.

An extension of the Adjusted Rand Index for fuzzy partitions / D'Ambrosio, Antonio; Amodio, S.; Iorio, Carmela; Siciliano, Roberta. - (2015). (Intervento presentato al convegno Internationl Federation of Classification Societies 2015 tenutosi a Bologna nel 6-8 Luglio 2015).

An extension of the Adjusted Rand Index for fuzzy partitions

D'AMBROSIO, ANTONIO;IORIO, CARMELA;SICILIANO, ROBERTA
2015

Abstract

Invited talk: To compare clustering partitions, Rand index (RI) and Adjusted Rand index (ARI) are commonly used for measuring the agreement between partitions. Both these external validation indexes aim to analyze how close is a cluster to a reference (or to prior knowledge about the data) by counting corrected classified pairs of elements. When the aim is to evaluate the solution of a fuzzy clustering algorithm, the computation of these measures require converting the soft partitions into hard ones. It is known that different fuzzy partitions describing very different structures in the data can lead to the same crisp partition and consequently to the same values of these measures. We compare the existing approaches to evaluate the external validation criteria in fuzzy clustering and we propose an extension of the ARI for fuzzy partitions based on the normalized degree of concordance, which we call Adjusted Concordance Index. Through use of real and simulated data, we analyze and evaluate the performance of our proposal.
2015
An extension of the Adjusted Rand Index for fuzzy partitions / D'Ambrosio, Antonio; Amodio, S.; Iorio, Carmela; Siciliano, Roberta. - (2015). (Intervento presentato al convegno Internationl Federation of Classification Societies 2015 tenutosi a Bologna nel 6-8 Luglio 2015).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/609257
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