Information technology of the multivariate time series fuzzy clustering on the example of the Samara river hydrochemical monitoring

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Authors:

O.G. Baibuz, Dr. Sci. (Tech.), Professor, Oles Honchar Dnipropetrovsk National University, Head of the Department of Software Design, Dnipropetrovsk, Ukraine

M.G. Sidorova, Oles Honchar Dnipropetrovsk National University, Postgraduate Student, Dnipropetrovsk, Ukraine

Abstract:

Purpose. Development of the methods for filling the information technology of fuzzy clustering in the case of multivariate time series.

Methodology. This paper presents a technique of cluster analysis of multivariate time series as a computational schemes based on the one-dimensional time series clustering, aggregating results into a similarity matrix and determination the result fuzzy partition.

Findings. Computational schemes of methods: agglomerative hierarchical, K-means, Forel, graph method of the shortest non-closed path have been adapted to the time series clustering and included in the core of the proposed information technology. Their quality has been assessed by different quality criteria. The practical implementation with the analysis of the results has been applied to the data of hydrochemical monitoring of technologically-laden area.

Originality. A new metric for comparing time series which takes into account both the nature of the compared series and the closeness of their values has been proposed, that can improve the quality of clustering. The information technology of multivariate time series clustering based on an ensemble approach and fuzzy logic has been proposed.

Practical value. On the basis of the proposed technology and developed software cluster analysis of data hydrochemical monitoring of surface waters of the West Donbass region (r. Samara) has been held. It has allowed to identify groups of control points, which characterized by similar physical and chemical composition of water on the investigated components for proper environmental planning and management of water quality of the river.

References:

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Yatskіv, I. and Gusarova, L., (2003), “Methods for determining the number of clusters in unsupervised classification”, Transport and Telecommunication, Vol.4, no. 1, pp. 23–28.

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ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
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