Functional Principal components direction to cluster earthquake waveforms
- Autori: Adelfio, G.; Chiodi, M.; D'Alessandro, A.; Luzio, D.
- Anno di pubblicazione: 2010
- Tipologia: Proceedings (TIPOLOGIA NON ATTIVA)
- Parole Chiave: FPCA, waveforms, clustering approach
- OA Link: http://hdl.handle.net/10447/52911
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))