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Performance Analysis of Unsupervised Classification based on Optimization

by K. Velusamy, R. Manavalan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 19
Year of Publication: 2012
Authors: K. Velusamy, R. Manavalan
10.5120/5802-8090

K. Velusamy, R. Manavalan . Performance Analysis of Unsupervised Classification based on Optimization. International Journal of Computer Applications. 42, 19 ( March 2012), 22-27. DOI=10.5120/5802-8090

@article{ 10.5120/5802-8090,
author = { K. Velusamy, R. Manavalan },
title = { Performance Analysis of Unsupervised Classification based on Optimization },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 19 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number19/5802-8090/ },
doi = { 10.5120/5802-8090 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:44.936726+05:30
%A K. Velusamy
%A R. Manavalan
%T Performance Analysis of Unsupervised Classification based on Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 19
%P 22-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Unsupervised classification is one of the primary research areas in data mining. Clustering algorithm partitions a data set into several groups based on the similarity. Quick reduct algorithm is used to find a minimal feature subset from the original feature space while retaining a suitably high accuracy in representing the original features. Fuzzy-C-Mean (FCM) clustering algorithm is one of the most popular clustering methods since it is an efficient, straightforward, easy to implement and sensitive to initialization. Since, the weakness is easily trapped in local optima. In this paper proposes hybrid Fuzzy c means with an evolutionary algorithm known as Ant Colony Algorithm (ACO) for clustering problem in order to flee from local optima by utilizing the merits of both algorithms FCM and ACO. The experimental results confirm the efficiency of the proposed method

References
  1. Mehdizadeh. E, Sadi-Nezhad. S and Tavakkoli-Moghaddam. R, Optimization of Fuzzy Clustering Criteria by a Hybrid PSO and Fuzzy C-Means Clustering Algorithm," Iranian Journal Of Fuzzy Systems Vol. 5, No. 3, (2008).
  2. Jeng-Ming Yih, Yuan-Horng Lin, Hsiang-Chuan Liu,"Clustering Analysis Method based on Fuzzy C-Means Algorithm of PSO and PPSO with Application in Image Data," ISBN: 978-960-474-028-4, Proceedings of the 8th WSEAS International Conference on Applied Computer Science (Acs'08).
  3. Hesam Izakian a, Ajith Abraham," Fuzzy C-means and fuzzy swarm for fuzzy clustering problem," 0957-4174 2010 Elsevier Ltd. , doi:10. 1016/j. eswa. 2010. 07. 112
  4. Selvi. V Dr. R. Umarani, "Comparative Analysis of Ant Colony and Particle Swarm Optimization Technique," International Journal of Computer Applications (0975 – 8887)Volume 5– No. 4, August 2010.
  5. Zhiding Yu, OscarC Au, RuobingZou, WeiyuYu, JingTian, "An adaptive unsupervised approach toward pixel clustering and color image segmentation," 0031-3203, 2009 Elsevier Ltd, doi:10. 1016/j. patcog. 2009. 11. 015
  6. Velayutham. C and Thangavel. K ," Unsupervised Quick Reduct Algorithm Rough Set Theory "Journal of Electronic Science and Technology, Vol. 9, No. 3, September 2011Hg
  7. Zhang. M, Yao J. T, "A Rough Sets Based Approach to Feature Selection, "University of Regina, Saskatchewan.
  8. Qiang Niu, Xinjian Huang,"An Improved Fuzzy C-means Clustering Algorithm based on PSO," Journal Of Software, Vol. 6, no. 5, may 2011.
  9. Peng. W, Wang. k, zhou. c, long. l ,"Fuzzy Disctrete Particle Swarm Optimization For Solving Traval Salsemen Person," In Prceedings Of The Fourth International Conference On Computer And Information Technolong, IEEE Cs Press, pp 796-800(2004).
  10. Hesam Izakian a, Ajith Abraham," Fuzzy C-means and fuzzy swarm for fuzzy clustering problem," 0957-4174 2010 Elsevier Ltd. , doi:10. 1016/j. eswa. 2010. 07. 112
  11. Chandra Mohan. B. Baskaran. R ,"A survey: Ant Colony Optimization based recent research and implementation on several engineering domain," 0957-4174, 2011 Elsevier Ltd.
  12. Yanfang Han_, Pengfei Shi, "An improved ant colony algorithm for fuzzy clustering in image segmentation," 0925-2312 Elsevier doi:10. 1016/j. neucom. 2006. 10. 022,2006
  13. Zhiding Yu, OscarC Au, RuobingZou, WeiyuYu, JingTian, "An adaptive unsupervised approach toward pixel clustering and color image segmentation," 0031-3203, 2009 Elsevier Ltd, doi:10. 1016/j. patcog. 2009. 11. 015.
  14. Chen Yanyun, Qiu Jianlin, Gu Xiang, Chen Jianping, Ji Dan Chen Li, "Advances in Research of Fuzzy C-Means Clustering Algorithm" , IEEE, 10. 1109/NCIS. 2011. 104.
  15. Szabo. A, de Castro. L. N," The proposal of a fuzzy clustering algorithm based on particle swarm", Nature and Biologically Inspired Computing (NaBIC), Third World Congress on 2011, IEEE, 978-1-4577-1122-0.
  16. Sanya, Hainan China," SVM Combined with FCM and PSO for Fuzzy Clustering, Seventh International Conference on Computational Intelligence and Security" IEEE, 978-0-7695-4584-4.
  17. Chun-Wei Tsai, Kai-Cheng Hu, Ming-Chao Chiang Chu-Sing Yang, "Ant colony optimization with dual pheromone tables for clustering" Fuzzy Systems (FUZZ), 2011 IEEE International Conference',978-1-4244-7315-1.
  18. Mohamed Jafar Abul Hasan and Sivakumar Ramakrishnan,"A survey: hybrid evolutionary algorithms for cluster analysis", Artificial Intelegent Review, 179-204, DOI: 10. 1007/s10462-011-9210-5
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Clustering Ant Colony Optimization Fuzzy C Means Particle Swarm Optimization

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