Abstract
We present an integrated methodology for the discovery of hidden relations and underlying indicative patterns in time-series collections. The methodology is realized by the smooch integration of: (i) dynamic and qualitative discretization of time-series data, (ii) matching time-series by respective similarity assessment operations, and (iii) a novel hierarchical clustering process, grounded on a graph-theoretic technique, which combines information about the distances between objects and their respective featurebased descriptions. We apply our methodology on in-vivo neuropsychological data targeting the challenging task of patterning brain-developmental events.
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Potamias, G. (2002). Distance and Feature-Based Clustering of Time Series: An Application on Neurophysiology. In: Vlahavas, I.P., Spyropoulos, C.D. (eds) Methods and Applications of Artificial Intelligence. SETN 2002. Lecture Notes in Computer Science(), vol 2308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46014-4_22
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DOI: https://doi.org/10.1007/3-540-46014-4_22
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