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Authors: Adelfio, Giada
Chiodi, Marcello
D'Alessandro, Antonino*
Luzio, Dario
Title: Functional Principal Components direction to cluster earthquake
Issue Date: 2-May-2010
Keywords: Waveforms clustering
Abstract: Looking for curves similarity could be a complex issue characterized by subjective choices related to continuous transformations of observed discrete data (Chiodi, 1989). In this paper we combine the aim of finding clusters from a set of individual curves to the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. We apply a classical clustering method to rotated data, according to the direction of maximum variance. A k-means clustering algorithm based on PCA rotation of data is proposed, as an alternative to methods that require previous interpolation of data based on splines or linear fitting (García-Escudero and Gordaliza (2005), Tarpey (2007), Sangalli et al. (2008)).
Appears in Collections:Conference materials
04.06.99. General or miscellaneous

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