Adaptive Fuzzy Clustering of Multivariate Short Time Series with Unevenly Distributed Observations Based on Matrix Neuro-Fuzzy Self-organizing Network

  • Galina Setlak
  • Yevgeniy BodyanskiyEmail author
  • Iryna Pliss
  • Olena Vynokurova
  • Dmytro Peleshko
  • Illya Kobylin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 643)


In the paper the method of fuzzy clustering task for multivariate short time series with unevenly distributed observations is proposed. Proposed method allows to process the time series both in batch mode and sequential on-line mode. In the first case we can use the matrix modification of fuzzy C-means method, and in second case we can use the matrix modification of neuro-fuzzy network by T. Kohonen, which is learned using the rule “Winner takes more”. Proposed fuzzy clustering algorithms are enough simple in computational implementation and can be used for solving of wide class of Big Data and Data Stream Mining problems. The effectiveness of proposed approach is confirmed by many experiments based on real data sets.


Adaptive fuzzy clustering Multivariate short time series Unevenly distributed observations Matrix neuro-fuzzy self-organizing network 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Galina Setlak
    • 1
  • Yevgeniy Bodyanskiy
    • 2
    Email author
  • Iryna Pliss
    • 2
  • Olena Vynokurova
    • 2
  • Dmytro Peleshko
    • 3
  • Illya Kobylin
    • 2
  1. 1.Rzeszow University of TechnologyRzeszowPoland
  2. 2.Kharkiv National University of Radio ElectronicsKharkivUkraine
  3. 3.University “IT Step Academy”LvivUkraine

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