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The Adaptive Fuzzy Meridian and Its Appliction to Fuzzy Clustering

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Computer Recognition Systems 3

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 57))

Summary

The fuzzy clustering methods are useful in the data mining field of applications. In this paper a new clustering method that deals with data described by the meridian distribution is presented. The fuzzy meridian is used as the cluster prototype. Simple computation method for the fuzzy meridian is given as well as the meridian medianity parameter. A numerical example illustrates the performance of the proposed method.

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References

  1. Huber, P.: Robust statistics. Wiley, New York (1981)

    Book  MATH  Google Scholar 

  2. Dave, R.N., Krishnapuram, R.: Robust Clustering Methods: A Unified View. IEEE Trans. on Fuzzy System 5, 270–293 (1997)

    Article  Google Scholar 

  3. Łeski, J.: An ε–Insensitive Approach to Fuzzy Clustering. Int. J. Appl. Math. Comput. Sci. 11, 993–1007 (2001)

    Google Scholar 

  4. Chatzis, S., Varvarigou, T.: Robust Fuzzy Clustering Using Mixtures of Student’s–t Distributions. Pattern Recognition Letters 29, 1901–1905 (2008)

    Article  Google Scholar 

  5. Frigui, H., Krishnapuram, R.: A Robust Competitive Clustering Algorithm With Applications in Computer Vision. IEEE Trans. Pattern Analysis and Machine Intelligence 21, 450–465 (1999)

    Article  Google Scholar 

  6. Kaufman, L., Rousseeuw, P.: Finding Groups in Data. Wiley–Interscience, Chichester (1990)

    Book  Google Scholar 

  7. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)

    Book  MATH  Google Scholar 

  8. Pedrycz, W.: Konwledge–Based Clustering. Wiley–Interscience, Chichester (2005)

    Google Scholar 

  9. Aysal, T.C., Barner, K.E.: Meridian Filtering for Robust Signal Processing. IEEE Trans. on Signal Proc. 55, 3949–3962 (2007)

    Article  MathSciNet  Google Scholar 

  10. Parzen, E.: On Estimation Of A Probability Density Function And Mode. Ann. Math. Stat. 33, 1065–1076 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  11. Kersten, P.R.: Fuzzy Order Statistics and Their Application to Fuzzy Clustering. IEEE Trans. On Fuzzy Sys. 7, 708–712 (1999)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Electronics, Silesian University of Technology, ul Akademicka 16, 44-101, Gliwice, Poland

    Tomasz Przybyla

  2. Departament of Biomedical Informatics, Institute of Medical Technology and Equipment ITAM, Roosvelta Str 118, 41-800, Zabrze, Poland

    Janusz Jezewski & Krzysztof Horoba

Authors
  1. Tomasz Przybyla
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  2. Janusz Jezewski
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  3. Krzysztof Horoba
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Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, WybrzezeWyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzynski  & Michal Wozniak  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Przybyla, T., Jezewski, J., Horoba, K. (2009). The Adaptive Fuzzy Meridian and Its Appliction to Fuzzy Clustering. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_30

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  • DOI: https://doi.org/10.1007/978-3-540-93905-4_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-93904-7

  • Online ISBN: 978-3-540-93905-4

  • eBook Packages: EngineeringEngineering (R0)

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