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A New Fuzzy Clustering Method with Constraints in Time Domain

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Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

This paper introduces a new fuzzy clustering method with constraints in time domain which may be used to signal analysis. Proposed method makes it possible to include a natural constraints for signal analysis using fuzzy clustering, that is, the neighbouring samples of signal belong to the same cluster. The well-known from the literature fuzzy c-regression models can be obtained as a special case of the method proposed in this paper.

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References

  1. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1982)

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  2. Hathaway, R.J., Bezdek, J.C.: Switching Regression Models and Fuzzy Clustering. IEEE Trans. Fuzzy Systems 1(3), 195–204 (1993)

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Author information

Authors and Affiliations

  1. Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Jacek Leski

  2. Department of Computer Medical Systems, Institute of Medical Technology and Equipment, Roosevelt St. 118A, 41-800, Zabrze, Poland

    Jacek Leski & Aleksander Owczarek

Authors
  1. Jacek Leski
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  2. Aleksander Owczarek
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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Leski, J., Owczarek, A. (2004). A New Fuzzy Clustering Method with Constraints in Time Domain. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_97

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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