Call for Paper
CAE solicits original research papers for the July 2023 Edition. Last date of manuscript submission is June 30, 2023.
Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering
Dmitri A Viattchenin and Stanislau Shyrai. Article: Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering. Communications on Applied Electronics 1(8):30-40, May 2015. Published by Foundation of Computer Science, New York, USA. BibTeX
@article{key:article, author = {Dmitri A. Viattchenin and Stanislau Shyrai}, title = {Article: Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering}, journal = {Communications on Applied Electronics}, year = {2015}, volume = {1}, number = {8}, pages = {30-40}, month = {May}, note = {Published by Foundation of Computer Science, New York, USA} }
Abstract
This paper introduces a novel intuitionistic fuzzy set-based heuristic algorithm of possibilistic clustering. For the purpose, some remarks on the fuzzy approach to clustering are discussed and a brief review of intuitionistic fuzzy set-based clustering procedures is given, basic concepts of the intuitionistic fuzzy set theory and the intuitionistic fuzzy generalization of the heuristic approach to possibilistic clustering are considered, a general plan of the proposed clustering procedure is described in detail, two illustrative examples confirm good performance of the proposed algorithm, and some preliminary conclusions are formulated.
Reference
- Zadeh, L. A. 1965. Fuzzy Sets. Information and Control. 8, 338-353.
- Krishnapuram, R. and Keller, J. M. 1993. A Possibilistic Approach to Clustering. IEEE Transactions on Fuzzy Systems. 1(1), 98-110.
- Bezdek, J. C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press.
- 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.
- Sato-Ilic, M. and Jain, L. C. 2006. Innovations in Fuzzy Clustering. Theory and Applications. Heidelberg: Springer.
- Miyamoto, S. , Ichihashi, H. and Honda, K. 2008. Algorithms for Fuzzy Clustering. Methods in C-Means Clustering with Applications. Heidelberg: Springer.
- Viattchenin, D. A. 2013. A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications. Heidelberg: Springer.
- Atanassov, K. T. 1986. Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems. 20, 87-96.
- Hung, W. -L. , Lee, J. -S. and Fuh, C. -D. 2004. Fuzzy Clustering Based on Intuitionistic Fuzzy Relations. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 12, 513-529.
- Xu, Z. , Chen, J. and Wu, J. 2008. Clustering Algorithm for Intuitionistic Fuzzy Sets. Information Science. , 178, 3775-3790.
- Cai, R. , Lei, Y. J. and Zhao, X. J. 2009. Clustering Method Based on Intuitionistic Fuzzy Equivalent Dissimilarity Matrix. Journal of Computer Applications. 29, 123-126.
- Wang, Z. , Xu, Z. , Liu, S. and Tang, J. 2011. A Netting Clustering Analysis Method under Intuitionistic Fuzzy Environment. Applied Soft Computing. 11, 5558-5564.
- Pelekis, N. , Iakovidis, D. K. , Kotsifakos, E. E. and Kopanakis, I. 2008. Fuzzy Clustering of Intuitionistic Fuzzy Data. International Journal of Business Intelligence and Data Mining. 3, 45-65.
- Iakovidis, D. K. , Pelekis, N. , Kotsifakos, E. E. and Kopanakis, I. 2008. Intuitionistic Fuzzy Clustering with Applications in Computer Vision. In Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS'2008), 764-774.
- Torra, V. , Miyamoto, S. , Endo, Y. and Domingo-Ferrer, J. 2008. On Intuitionistic Fuzzy Clustering for its Application to Privacy. In Proceedings of the 2008 IEEE World Congress on Computational Intelligence (WCCI'2008) and the 17th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'08), 1042-1048.
- Karthikeyani Visalakshi, N. , Thangavel, K. and Parvathi, R. 2010. An Intuitionistic Fuzzy Approach to Distributed Fuzzy Clustering. International Journal of Computer Theory and Engineering. 2, 295-302.
- Todorova, L. and Vassilev, P. 2010. Algorithm for Clustering Data Set Represented by Intuitionistic Fuzzy Estimates. International Journal of Bioautomation. 14, 61-68.
- Xu, Z. 2009. Intuitionistic Fuzzy Hierarchical Clustering Algorithms. Journal of Systems Engineering and Electronics. 20, 1-8.
- Xu, Z. and Wu, J. 2010. Intuitionistic Fuzzy C-Means Clustering Algorithms. Journal of Systems Engineering and Electronics. 21, 580-590.
- Yan, C. and Chen, A. -D. 2012. A FCM Algorithm Based on Weighted Intuitionistic Fuzzy Set. International Journal of Digital Content Technology and Its Applications. 6, 95-101.
- Chaudhuri, A. 2015. Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms. Advances in Fuzzy Systems. 2015, 1-17.
- Pal, N. R. , Pal, K. and Bezdek, J. C. 1997. A Mixed C-Means Clustering Model. In Proceedings of the 6th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'97), Vol. 1, 11-21.
- Xu, Z. 2013. Intuitionistic Fuzzy Aggregation and Clustering. Heidelberg: Springer.
- Burillo, P. and Bustince, H. 1995. Intuitionistic Fuzzy Relations (Part I). Mathware and Soft Computing. 2, 5-38.
- Burillo, P. and Bustince, H. 1995. Intuitionistic Fuzzy Relations (Part II) Effect of Atanassov's Operators on the Properties of the Intuitionistic Fuzzy Relations. Mathware and Soft Computing. 2, 117-148.
- Zimmermann, H. -J. 1991. Fuzzy Set Theory and Its Applications. Boston: Kluwer Academic Publishers.
- Varlamov, O. O. 2002. Evolutional Data and Knowledge Bases for an Adaptive Synthesis of Intelligent Systems. MIVAR Information Space. Moscow: Radio I Svyaz. (in Russian)
- Varlamov, O. O. 2011. MIVAR Technologies of the Development of Intelligent Systems and the Creation of the Active Multi-Subject Online MIVAR Encyclopedia. In Proceedings of the 11th International Conference on Pattern Recognition and Information Processing (PRIP'2011), 326-329.
Keywords
Intuitionistic Fuzzy Set, Possibilistic Clustering, Allotment among Intuitionistic Fuzzy Clusters, Typical Point.