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 of Categorical Data / Marino, Marina; Palumbo, Francesco; Tortora, C.. - (2012), pp. 13-22. [10.1007/978-3-642-21037-2_2]
Clustering in Feature Space for Interesting Pattern Identification of Categorical Data
MARINO, MARINA;PALUMBO, FRANCESCO;
2012
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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