A fuzzy c means variant for clustering evolving data streams

  title={A fuzzy c means variant for clustering evolving data streams},
  author={Prodip Hore and Lawrence O. Hall and Dmitry B. Goldgof},
  journal={2007 IEEE International Conference on Systems, Man and Cybernetics},
Clustering algorithms for streaming data sets are gaining importance due to the availability of large data streams from different sources. Recently a number of streaming algorithms have been proposed using crisp algorithms such as hard c means or its variants. The crisp cases may not be easily generalized to fuzzy cases as these two groups of algorithms try to optimize different objective functions. In this paper we propose a streaming variant of the fuzzy c means algorithm. At any stage during… CONTINUE READING
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  • M. Jenkinson, M. Pechaud, S. Smith
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  • 2005
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Hathaway and James C . Bezdek , Extending Fuzzy and Probabilistic Clustering to Very Large Data Sets ,

  • J. Richard
  • Journal of Computational Statistics and Data…
  • 2006

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  • Jurgen Beringer, Eyke
  • Data Knowl. Eng, V
  • 2006
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