Comparative Analysis of Noisy Time Series Clustering

Кіріченко, Л. О.; Радівілова, Т. А.; Ткаченко, А. Є.

Публікація:
Comparative Analysis of Noisy Time Series Clustering

dc.contributor.author	Кіріченко, Л. О.
dc.contributor.author	Радівілова, Т. А.
dc.contributor.author	Ткаченко, А. Є.
dc.date.accessioned	2019-06-19T08:52:30Z
dc.date.available	2019-06-19T08:52:30Z
dc.date.issued	2019
dc.description.abstract	A 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.citation	Kirichenko 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.uri	http://openarchive.nure.ua/handle/document/9456
dc.language.iso	en	uk_UA
dc.subject	Time Series Clustering	uk_UA
dc.subject	DBSCAN Method	uk_UA
dc.subject	Atypical Time Series	uk_UA
dc.subject	Noisy Time Series Clustering	uk_UA
dc.title	Comparative Analysis of Noisy Time Series Clustering	uk_UA
dc.type	Thesis	uk_UA
dspace.entity.type	Publication