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A Novel Direct Relational Heuristic Algorithm of Possibilistic Clustering

by Dmitri A. Viattchenin, Aliaksandr Yaroma, Aliaksandr Damaratski
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 18
Year of Publication: 2014
Authors: Dmitri A. Viattchenin, Aliaksandr Yaroma, Aliaksandr Damaratski
10.5120/18850-0381

Dmitri A. Viattchenin, Aliaksandr Yaroma, Aliaksandr Damaratski . A Novel Direct Relational Heuristic Algorithm of Possibilistic Clustering. International Journal of Computer Applications. 107, 18 ( December 2014), 15-21. DOI=10.5120/18850-0381

@article{ 10.5120/18850-0381,
author = { Dmitri A. Viattchenin, Aliaksandr Yaroma, Aliaksandr Damaratski },
title = { A Novel Direct Relational Heuristic Algorithm of Possibilistic Clustering },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 18 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number18/18850-0381/ },
doi = { 10.5120/18850-0381 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:23.941226+05:30
%A Dmitri A. Viattchenin
%A Aliaksandr Yaroma
%A Aliaksandr Damaratski
%T A Novel Direct Relational Heuristic Algorithm of Possibilistic Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 18
%P 15-21
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper deals with the problem of a heuristic approach to possibilistic clustering. The approach is based on the concept of allotment among fuzzy clusters. The paper provides the description of basic definitions of the heuristic approach to possibilistic clustering. A new parameter for algorithms is introduced and a new relational algorithm for unknown number of fuzzy clusters is proposed. An illustrative example of application of the proposed algorithm to Tamura's portrait data set is considered. 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. Chichester: John Wiley & Sons.
  2. Krishnapuram, R. and Keller, J. M. 1993. A Possibilistic Approach to Clustering. IEEE Transactions on Fuzzy Systems. 1(1), 98-110.
  3. Viattchenin, D. A. 2013. A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications. Heidelberg: Springer.
  4. Viattchenin, D. A. , Damaratski, A. and Yaroma, A. 2014. New Parameters for Relational Heuristic Algorithms of Possibilistic clustering. Proceedings of the 12th International Conference PRIP'2014, 295-299.
  5. Miyamoto, S. , Ichihashi, H. and Honda K. 2008. Algorithms for Fuzzy Clustering: Methods in C-Means Clustering with Applications. Heidelberg: Springer.
  6. Tamura, S. , Higuchi, S. and Tanaka, K. 1971. Pattern Classification Based on Fuzzy Relations. IEEE Transactions on Systems, Man, and Cybernetics. 1(1), 61-66.
  7. Viattchenin, D. A. 2010. Derivation of Fuzzy Rules from Interval-Valued Data. International Journal of Computer Applications. 7(3), 13-20.
  8. Viattchenin, D. A. 2012. On Constructing Fuzzy Classifiers from Interval-Valued Data in Case of Unstable Clustering Structure. Control Engineering and Applied Informatics. 14(4), 17-26.
  9. Kacprzyk, J. , Owsinski, J. W. , and Viattchenin, D. A. 2014. Extracting Fuzzy Classification Rules from Three-Way Data. Journal of Automation, Mobile Robotics and Intelligent Systems. 8(2), 47-57.
Index Terms

Computer Science
Information Sciences

Keywords

Possibilistic Clustering Fuzzy Tolerance Fuzzy Cluster Allotment Cluster Size.

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