Abstract
The interrelation between data channels of multivariate data sets may lead to cluster formation. Revealing the cluster structure can give important information about the underlying systems' properties. Here we investigate the features of a recent genuinely multivariate cluster detection algorithm that is suitable for time-resolved and unsupervised application to nonstationary and noisy time series. Using numerical test systems it is discussed under which conditions intra- and inter-cluster relations can be disentangled and quantified. In addition different types of errors occurring when channels are automatically attributed to clusters are investigated quantitatively. Finally, the algorithm is applied to nonstationary model time series and its time-dependent performance is compared to other cluster detection algorithms.
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