Despite its large use, one major limitation of K-means algorithm is the impact of the initial seeding on the final partition. We propose a modified version, using the information contained in a co-association matrix obtained from clustering ensembles; such matrix is given as input for a set of pivotal methods, implemented in the pivmet R package, used to perform a pivot-based initialization step. Preliminary results concerning the comparison with the classical approach and other clustering methods are discussed.

Pivotal seeding for K-means based on clustering ensembles

Leonardo Egidi;Roberta Pappadà;Francesco Pauli;Nicola Torelli
2019-01-01

Abstract

Despite its large use, one major limitation of K-means algorithm is the impact of the initial seeding on the final partition. We propose a modified version, using the information contained in a co-association matrix obtained from clustering ensembles; such matrix is given as input for a set of pivotal methods, implemented in the pivmet R package, used to perform a pivot-based initialization step. Preliminary results concerning the comparison with the classical approach and other clustering methods are discussed.
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https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Dirigenti e istituzioni/ISTITUZIONI-HE-PDF-sis2019_V4.pdf
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2946994
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