Standard clustering methods fail when data are characterized by non-linear associations. A suitable solution consists in mapping data in a higher dimensional feature space where clusters are separable. The aim of the present contribution is to propose a new technique in this context and to compare it with k-means technique.

A comparison between K-means and Support Vector Clustering for Categorical Data / Marino, Marina; Tortora, Cristina. - In: STATISTICA APPLICATA. - ISSN 1125-1964. - STAMPA. - 21:1(2009), pp. 5-16.

A comparison between K-means and Support Vector Clustering for Categorical Data

MARINO, MARINA;TORTORA, CRISTINA
2009

Abstract

Standard clustering methods fail when data are characterized by non-linear associations. A suitable solution consists in mapping data in a higher dimensional feature space where clusters are separable. The aim of the present contribution is to propose a new technique in this context and to compare it with k-means technique.
2009
A comparison between K-means and Support Vector Clustering for Categorical Data / Marino, Marina; Tortora, Cristina. - In: STATISTICA APPLICATA. - ISSN 1125-1964. - STAMPA. - 21:1(2009), pp. 5-16.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/365443
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