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 to identify interesting patterns in large datasets.
Clustering in feature space for interesting pattern identification / Marino, Marina; Palumbo, Francesco; C., Tortora. - STAMPA. - (2009), pp. 459-462. (Intervento presentato al convegno Statistical Methods for the Analysis of Large Data-Sets tenutosi a Pescara nel 23-25 Settembre 2009).
Clustering in feature space for interesting pattern identification.
MARINO, MARINA;PALUMBO, FRANCESCO;
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 to identify interesting patterns in large datasets.File in questo prodotto:
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