Rough Fuzzy Clustering Algorithm Using Fuzzy Rough Correlation Factor


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


There are advantages to both fuzzy set and rough set theories, Combining these two and used for clustering gives better results. Rough clustering is less restrictive than hard clustering and less descriptive than fuzzy clustering. Rough clustering is an appropriate method since it separates the objects that are definite members of a cluster from the objects that are only possible members of a cluster. In fuzzy clustering similarities are described by membership degrees while in rough clustering definite and possible members to a cluster are detected. Fuzzy Rough Correlation Factor is the threshold for degree of fuzziness. It determines how low a DFR value shall be for it to be considered for cluster membership assignment. This paper proposes new modified rough fuzzy clustering algorithm based on fuzzy rough correlation factor. Hence rough fuzzy clustering can be derived directly from the results obtained thro fuzzy clustering.
Copyright © 2013 Praise Worthy Prize - All rights reserved.

Keywords


Fuzzy Clustering; Fuzzy Rough Correlation Factor; Rough Fuzzy Clustering

Full Text:

PDF


References


Z. Pawlak, “Rough Sets,” Int’l J. Information and Computer Sciences, vol. 11, pp. 145-172, 1982.

Z. Pawlak, Rough sets-Theoretical aspects of reasoning about data, Kluwar Academic Pulishers, Dorfrecht,1991.

P. Lingras, Rough set clustering for web mining, Proceedingsof the 2002 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’02), Vol: 2, pp.1039-1044.

P. Lingras, “Unsupervised Rough Set Classification Using GAs,” J. Intelligent Information Systems, vol. 16, no. 3, pp. 215-228, 2001.

Y.Y. Yao, “Information Granulation and Approximation in a Decision-Theoretical Model of Rough Sets,” Rough-Neuro Computing:Techniques for Computing with Words, pp. 491-516, Springer,2003

S. Mitra, “An evolutionary rough partitive clustering,” Pattern Recognit.Lett., vol. 25, no. 12, pp. 1439–1449, Sep. 2004.

Sushmita Mitra, Haider Banka,Witold Pedrycz,” Rough–Fuzzy Collaborative Clustering “,ieee transactions on systems, man, and cybernetics—part b: cybernetics, vol. 36, no. 4, pp 795-803, 2006.

D. Wang, and M. D. Wu, “Rough fuzzy c-means clustering algorithm and its application to image”, Journal of National University of Defense Technology, Vol. 29, No. 2, pp. 76-80, 2007.

Y.Y. Yao, “Decision-Theoretic Rough Set Models,” Lecture Notes in Computer Science, vol. 4481, pp. 1-12, 2007.

P. Lingras, M. Chen, and D.Q. Miao, “Rough Multi-Category Decision Theoretic Framework,” Rough Sets and Knowledge Technology, pp. 676-683, Springer, 2008.

P. Lingras, “Applications of Rough Set Based K-Means, Kohonen, GA Clustering,” Trans. Rough Sets, vol. 7, pp. 120-139, 2007.

P. Lingras, M. Chen, and D.Q. Miao, “Rough Multi-Category Decision Theoretic Framework,” Rough Sets and Knowledge Technology, pp. 676-683, Springer, 2008.

M. Sassi, A. Grissa Touzi, R. Chammem, A Visual Clustering Platform for Cluster’s Interpretation and Quality Evaluation, (2008) International Review on Computers and Software (IRECOS), 3 (3), pp. 226-233.

Peng, Y., Wan, H., A fuzzy-rough algorithm of computer practice teaching evaluation under engineering concepts, (2012) International Review on Computers and Software (IRECOS), 7 (4), pp. 1830-1834.

Kong, F., Hao, H., Zuo, J., Classification and dynamic fuzzy clustering of mold resources based on mold manufacturing grid platform, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2447-2452.

S.Revathy ,B.Parvathavarthini,”Integrating Rough clustering with Fuzzy System”,International Conference on Sustainable Energy and Intelligent System,2011 pp:865-869.

S.Revathy ,B.Parvathavarthini,”Comparison Study of Fuzzy Cluster Validity Indices”,National Conference on Emerging Trends in Information and Communication Technologies”,Mar 2012 pp:30-33.

S.Revathy ,B.Parvathavarthini,”Decision Theoretic Evaluation of Crisp and Rough Clustering”,National Journal of Advances in Computational Sciences and Technology, 2012,pp:131-142.

S.Revathy ,B.Parvathavarthini,”Comparison of FCM and RFCM on Iris Dataset Using Matlab”,International Conference on Computer Networks and information Technology,Bankok,2013,pp:12-15.

N. Torabi, M. Karrari, M. B.Menhaj, A Novel Wavelet Fuzzy Fault Location Method for Partially Observable Transmission Networks Based on WAMS/PMU, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (6), pp. 478-487.

A. Naseri, O. Azmoon, The Proposed Algorithm for De-Interleaving of Radar Interleaved Pulse Trains by Information Theory and Fuzzy C-Means, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (5), pp. 411-417.


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize