Cluster-based stability evaluation in time series data sets

In modern data analysis, time is often considered just another feature. Yet time has a special role that is regularly overlooked. Procedures are usually only designed for time-independent data and are therefore often unsuitable for the temporal aspect of the data. This is especially the case for clu...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 53; H. 13; S. 16606 - 16629
Hauptverfasser:	Klassen, Gerhard, Tatusch, Martha, Conrad, Stefan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.07.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Clustering Computer Science Data analysis Datasets Evaluation Evolutionary algorithms Machines Manufacturing Mechanical Engineering Outliers (statistics) Parameters Processes Stability analysis Time dependence Time series Toolkits Evolutionary clustering Over-time stability evaluation Time series clustering Anomalous subsequences
ISSN:	0924-669X, 1573-7497, 1573-7497
Online-Zugang:	Volltext
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