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Updated: Jun 25, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Data-driven estimates of the number of clusters in multivariate time series
Christian Rummel1, Markus Müller, Kaspar Schindler
1Department of Neurology, University Hospital and University of Bern, 3010 Bern, Switzerland. crummel@web.de
Abstract:
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.
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