Unsupervised System for Discovering Patterns in Time-Series

Abstract

Within this paper we present a framework for discovering patterns in time-series by unsupervised feature selection and unsupervised, self-organised clustering. The proposed unsupervised feature selection algorithm is determining the feature relevance for a variety of transformations to select a set of features to build the feature space. We propose to take… (More)

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Cite this paper

@inproceedings{Scherbart2006UnsupervisedSF, title={Unsupervised System for Discovering Patterns in Time-Series}, author={Alexandra Scherbart and Nils Goerke}, year={2006} }