Data-driven estimates of the number of clusters in multivariate time series

Rummel, Christian; Müller, Markus; Schindler, Kaspar (2008). Data-driven estimates of the number of clusters in multivariate time series. Physical review. E - statistical, nonlinear, and soft matter physics, 78(6 Pt 2), p. 66703. Melville, N.Y.: American Physical Society 10.1103/PhysRevE.78.066703

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An important problem in unsupervised data clustering is how to determine the number of clusters. Here we investigate how this can be achieved in an automated way by using interrelation matrices of multivariate time series. Two nonparametric and purely data driven algorithms are expounded and compared. The first exploits the eigenvalue spectra of surrogate data, while the second employs the eigenvector components of the interrelation matrix. Compared to the first algorithm, the second approach is computationally faster and not limited to linear interrelation measures.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology 04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Gynaecology
UniBE Contributor:	Rummel, Christian, Mueller, Michael, Schindler, Kaspar Anton
ISSN:	1539-3755
ISBN:	19256977
Publisher:	American Physical Society
Language:	English
Submitter:	Factscience Import
Date Deposited:	04 Oct 2013 15:05
Last Modified:	02 Mar 2023 23:22
Publisher DOI:	10.1103/PhysRevE.78.066703
PubMed ID:	19256977
Web of Science ID:	000262240600087
URI:	https://boris.unibe.ch/id/eprint/28493 (FactScience: 120990)