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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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  3. Pedrycz, W.: Distributed Collaborative Knowledge Elicitation. Computer Assisted Mechanics and Engineering Sciences 9, 87–104 (2002)

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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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