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Heuristic possibilistic clustering for detecting optimal number of elements in fuzzy clusters Cover

Heuristic possibilistic clustering for detecting optimal number of elements in fuzzy clusters

Open Access
|Mar 2016

References

  1. [1] Anderson E., The irises of the Gaspe Peninsula,,, 1, 1935, 2-5.
  2. [2] Bezdek J.C.,, Plenum Press, New York, 1981.
  3. [3] Chiang J.-H., Yue S., Yin Z.-X., A new fuzzy cover approach to clustering,,, 2, 2004, 199-208.
  4. [4] Corsini P., Lazzerini B., Marcelloni F., A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm,,, 6, 2005, 439-447.
  5. [5] De Cáceres M., Oliva F., Font X., On relational possibilistic clustering,,, 11, 2006, 2010-2024.
  6. [6] Everitt B.S., Landau S., Leese M., Stahl D.,, Wiley, Chichester, 2011.
  7. [7] Hamasuna Y., Endo Y., Miyamoto S., Fuzzy-means clustering for data with clusterwise tolerance based on- and-regularization,,, 1, 2011, 68-75.
  8. [8] Höppner F., Klawonn F., Kruse R., Runkler T.,, John Wiley & Sons, Chichester, 1999.
  9. [9] Kaufmann A.,, Academic Press, New York, 1975.
  10. [10] Komazaki Y., Miyamoto S., Variables for controlling cluster sizes on fuzzy-means, in: V. Torra, Y. Narukawa, G. Navarro-Arribas, D. Megías (eds.),, Springer, Berlin, 2013, 192-203.
  11. [11] Krishnapuram R., Keller J.M., A possibilistic approach to clustering,,, 2, 1993, 98-110.
  12. [12] Łęski J.M., Robust possibilistic clustering,,, 3/4, 2000, 141-155.
  13. [13] Mandel I.D.,, Finansy i Statistica, Moscow, 1988. (in Russian)
  14. [14] Ménard M., Courboulay V., Dardignac P.-A., Possibilistic and probabilistic fuzzy clustering: unification within the framework of the non-extensive thermostatistics,,, 6, 2003, 1325-1342.
  15. [15] Miyamoto S., Ichihashi H., Honda K.,, Springer, Berlin, 2008.
  16. [16] Miyamoto S., Different objective functions in fuzzy C-means algorithms and kernel-based clustering,,, 2, 2011, 89-97.
  17. [17] Pedrycz W., Fuzzy sets in pattern recognition: methodology and methods,,, 1/2, 1990, 121-146.
  18. [18] Sato-Ilic M., Jain L.C.,, Springer, Berlin, 2006.
  19. [19] Sneath P.H.A., Sokal R.,, Freeman, San Francisco, 1973.
  20. [20] Tamura S., Higuchi S., Tanaka, K., Pattern classification based on fuzzy relations,,, 1, 1971, 61-66.
  21. [21] Vapnik V.N.,, Wiley, New York, 1998.
  22. [22] Viattchenin D.A.,, Springer, Berlin, 2013.
  23. [23] Viattchenin D.A., Damaratski A., Direct heuristic algorithms of possibilistic clustering based on transitive approximation of fuzzy tolerance,,, 3, 2013, 5-15.
  24. [24] Viattchenin D.A., Yaroma A., Damaratski A., A novel direct relational heuristic algorithm of possibilistic clustering,,, 18, 2014, 15-21.
  25. [25] Walesiak M.,, Wydawnictwo Akademii Ekonomicznej im. Oskara Langego, Wrocław, 2002. (in Polish)
  26. [26] Xie Z., Wang S.T., Chung F.L., An enhanced possibilistic c-means clustering algorithm EPCM,,, 6, 2008, 593-611.
  27. [27] Yang M.-S., Wu K.-L., Unsupervised possibilistic clustering,,, 1, 2006, 5-21.
DOI: https://doi.org/10.1515/fcds-2016-0003 | Journal eISSN: 2300-3405 (formerly 0867-6356) | Journal ISSN: 0867-6356
Language: English
Page range: 45 - 76
Submitted on: Mar 24, 2015
Accepted on: Jan 20, 2016
Published on: Mar 31, 2016
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services

© 2016 Dmitri A. Viattchenin, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.