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Derivation of Fuzzy Rules from Interval-Valued Data

by Dmitri A. Viattchenin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 3
Year of Publication: 2010
Authors: Dmitri A. Viattchenin
10.5120/1146-1500

Dmitri A. Viattchenin . Derivation of Fuzzy Rules from Interval-Valued Data. International Journal of Computer Applications. 7, 3 ( September 2010), 13-20. DOI=10.5120/1146-1500

@article{ 10.5120/1146-1500,
author = { Dmitri A. Viattchenin },
title = { Derivation of Fuzzy Rules from Interval-Valued Data },
journal = { International Journal of Computer Applications },
issue_date = { September 2010 },
volume = { 7 },
number = { 3 },
month = { September },
year = { 2010 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume7/number3/1146-1500/ },
doi = { 10.5120/1146-1500 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:55:26.131806+05:30
%A Dmitri A. Viattchenin
%T Derivation of Fuzzy Rules from Interval-Valued Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 7
%N 3
%P 13-20
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fuzzy inference systems are widely used for classification and control. They can be designed from the training data. This paper describes a technique for deriving fuzzy classification rules from the interval-valued data. The technique based on a heuristic method of possibilistic clustering and a special method of the interval-valued data preprocessing. Basic concepts of the heuristic method of possibilistic clustering based on the allotment concept are described and the method of the interval-valued data preprocessing is also given. The method of constructing of fuzzy rules based on clustering results is presented. An illustrative example of the method’s application to the Sato and Jain’s interval-valued data is carried out. Preliminary conclusions are formulated.

References
  1. Höppner, F., Klawonn, F., Kruse, R. and Runkler, T. 1999. Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. John Wiley & Sons.
  2. Kreinovich, V. 2009. Interval computations and interval-related statistical techniques: tools for estimating uncertainty of the results of data processing and indirect measurements. In: Data Modeling for Metrology and Testing in Measurement Science, F. Pavese and A. B. Forbes (Eds.)
  3. Kreinovich, V. and Kosheleva, O. 2009. Towards dynamical systems approach to fuzzy clustering. In: Developments in Fuzzy Clustering, D.A. Viattchenin (Ed.)
  4. Mamdani, E.H. and Assilian, S. 1975. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.
  5. Sato, M., Sato, Y. 1994. On a multicriteria fuzzy clustering method for 3-way data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2(2), 127-142.
  6. Sato-Ilic, M. and Jain, L.C. 2006. Innovations in Fuzzy Clustering: Theory and Applications. Springer-Verlag.
  7. Viattchenin, D.A. 1999. Application of feeble similarity relation to data representation for pattern classification problems. In: Proceedings of the 6th International Conference ACS’99.
  8. Viattchenin, D.A. 2004. A new heuristic algorithm of fuzzy clustering. Control & Cybernetics, 33(2), P. 323-340.
  9. Viattchenin, D.A. 2007. Direct algorithms of fuzzy clustering based on the transitive closure operation and their application to outliers detection. Artificial Intelligence, 3, 205-216. (in Russian)
  10. Viattchenin, D.A. 2007. A direct algorithm of possibilistic clustering with partial supervision. Journal of Automation, Mobile Robotics and Intelligent Systems, 1(3), 29-38.
  11. Viattchenin, D.A. 2008. On possibilistic interpretation of membership values in fuzzy clustering method based on the allotment concept. Proceedings of the Institute of Modern Knowledge, 36(3), 85-90. (in Russian)
  12. Viattchenin, D.A. 2008. Kinds of fuzzy α-clusters. Proceedings of the Institute of Modern Knowledge, 37(4), 95-101. (in Russian)
  13. Viattchenin, D.A. 2008. A heuristic approach to possibilistic clustering for fuzzy data. Journal of Information and Organizational Sciences, 32(2), 149-163.
  14. Viattchenin, D.A. 2009. An outline for a heuristic approach to possibilistic clustering of the three-way data. Journal of Uncertain Systems, 3(1), 64-80.
  15. Viattchenin, D.A. 2009. An algorithm for detecting the principal allotment among fuzzy clusters and its application as a technique of reduction of analyzed features space dimensionality. Journal of Information and Organizational Sciences, 33(1), 205-217.
  16. Viattchenin, D.A. 2009. Analysis of the cluster structure robustness in nonstationary clustering problems. Doklady BGUIR, 44(6), 91-98. (in Russian)
  17. Viattchenin, D.A. and Damaratski, A. 2010. Constructing of allotment among fuzzy clusters in case of quasi-robust custer structure of set of objects. Doklady BGUIR, 47(1), 46-52. (in Russian)
  18. Viattchenin, D.A. 2010. Automatic generation of fuzzy inference systems using heuristic possibilistic clustering. Journal of Automation, Mobile Robotics and Intelligent Systems, 4(3), 36-44.
  19. Yang, M.-S. and Ko, C.-H. 1996. On a class of fuzzy c-numbers clustering procedures for fuzzy data. Fuzzy Sets and Systems, 84(1), 49-60.
  20. Žák, L. 2002. Clustering of vaguely defined objects. Archivum Mathematicum, 38(1), 37-50.
Index Terms

Computer Science
Information Sciences

Keywords

Possibilistic clustering fuzzy cluster typical point tolerance threshold fuzzy classification rule interval-valued data

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