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Functional Principal components direction to cluster earthquake waveforms

  • Autori: Adelfio, G; Chiodi, M; D'Alessandro, A; Luzio, D
  • Anno di pubblicazione: 2010
  • Tipologia: eedings
  • Parole Chiave: FPCA, waveforms, clustering approach
  • OA Link:


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 (Garc´ıa- Escudero and Gordaliza (2005), Tarpey (2007), Sangalli et al. (2008))