Abstract
Clustering algorithms belong to a category of unsupervised learning methods which aim to discover underlying structure in a dataset without given labels. We carry out research of methods for an analysis of a biological time series signals, putting stress on global patterns found in samples. When clustering raw time series data, high dimensionality of input vectors, correlation of inputs, shift or scaling sensitivity often deteriorates the result. In this paper, we propose to represent time series signals by various parametric models. A significant parameters are determined by means of heuristic methods and selected parameters are used for clustering. We applied this method to the data of cell’s impedance profiles. Clustering results are more stable, accurate and computationally less expensive than processing raw time series data.
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Bartoň, T., Kordík, P. (2013). Encoding Time Series Data for Better Clustering Results. In: Herrero, Á., et al. International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions. Advances in Intelligent Systems and Computing, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33018-6_48
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DOI: https://doi.org/10.1007/978-3-642-33018-6_48
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