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|Authors: ||Adelfio, Giada|
|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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|EGU2010-10344-1.pdf||EGU Abstract||99.67 kB||Adobe PDF||View/Open
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