Публікація:
Comparative Analysis of Noisy Time Series Clustering

dc.contributor.authorКіріченко, Л. О.
dc.contributor.authorРадівілова, Т. А.
dc.contributor.authorТкаченко, А. Є.
dc.date.accessioned2019-06-19T08:52:30Z
dc.date.available2019-06-19T08:52:30Z
dc.date.issued2019
dc.description.abstractA comparative analysis of the clustering of sample time series was performed. The clustering sample contained time series of various types, among which atypical objects were present. In the numerical experiment, white noise with different variance was added to the time series. Clustering was performed by k-means and DBSCAN methods using various similarity functions of time series. The values of the quality functionals were quantitative measures of the quality of clustering. The best results were shown by the DBSCAN method using the Euclidean metric with a Complexity Invariant Distance. The method allows to separate a cluster with atypical series at different levels of additive noise. The results of the clustering of real time series confirmed the applicability of the DBSCAN method for detecting anomaly.uk_UA
dc.identifier.citationKirichenko L. Comparative Analysis of Noisy Time Series Clustering / L. Kirichenko L., T. Radivilova, A. Tkachenko // Computational Linguistics and Intelligent Systems : proceedings of the 3rd International Conference, April 18-19, 2019. – Kharkiv, 2019. – Volume I. – P.84-196.uk_UA
dc.identifier.urihttp://openarchive.nure.ua/handle/document/9456
dc.language.isoenuk_UA
dc.subjectTime Series Clusteringuk_UA
dc.subjectDBSCAN Methoduk_UA
dc.subjectAtypical Time Seriesuk_UA
dc.subjectNoisy Time Series Clusteringuk_UA
dc.titleComparative Analysis of Noisy Time Series Clusteringuk_UA
dc.typeThesisuk_UA
dspace.entity.typePublication

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