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Adaptive Fuzzy Clustering of Short Time Series with Unevenly Distributed Observations in Data Stream Mining Tasks

Author

Listed:
  • Bodyanskiy Yevgeniy

    (Kharkiv National University of Radio Electronics, Latvi)

  • Vynokurova Olena
  • Kobylin Ilya
  • Kobylin Oleg

    (Kharkiv National University of Radio Electronics, Latvia)

Abstract

In the paper, adaptive modifications of fuzzy clustering methods have been proposed for solving the problem of data stream mining in online mode. The clustering-segmentation task of short time series with unevenly distributed observations (at the same time in all samples) is considered. The proposed approach for adaptive fuzzy clustering of data stream is sufficiently simple in numerical implementation and is characterised by a high speed of information processing. The computational experiments have confirmed the effectiveness of the developed approach.

Suggested Citation

  • Bodyanskiy Yevgeniy & Vynokurova Olena & Kobylin Ilya & Kobylin Oleg, 2016. "Adaptive Fuzzy Clustering of Short Time Series with Unevenly Distributed Observations in Data Stream Mining Tasks," Information Technology and Management Science, Sciendo, vol. 19(1), pages 23-28, December.
    • Handle: RePEc:vrs:itmasc:v:19:y:2016:i:1:p:23-28:n:6
    DOI: 10.1515/itms-2016-0006
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