Ant colony clustering and classification
| International Journal of Computer Science and Engineering |
| © 2015 by SSRG - IJCSE Journal |
| Volume 2 Issue 10 |
| Year of Publication : 2015 |
| Authors : Surya.K |
10.14445/23488387/IJCSE-V2I10P101
How to Cite?
Surya.K, "Ant colony clustering and classification," SSRG International Journal of Computer Science and Engineering , vol. 2, no. 10, pp. 1-4, 2015. Crossref, https://doi.org/10.14445/23488387/IJCSE-V2I10P101
Abstract:
In this paper, to produce the system performance higher, a new algorithm is developed to combine clustering and classifying technique. On considering the objects, it may be similar or dissimilar. The process of organizing objects based on whether the objects are similar or dissimilar is done by CLUSTERING technique and CLASSIFICATION which helps to classify the data objects when the data is large. In this paper clustering and classification is done by class values and attributes. It adopts the improved ANT COLONY algorithm as the method of selection classification characteristics parameters. Totally it includes five modules to show the accuracy and effectiveness of the proposed system.
Keywords:
clustering, classifying, Class values, Attributes, Ant colony algorithm.
References:
1. A. Colorni, M. Dorigo et V. Maniezzo, Distributed Optimization by Ant Colonies, actes de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 2010.
2. B. Pfahring, "Multi-agent search for open scheduling: adapting the Ant-Q formalism," Technical report TR-96-09, 2006
3. Dorigo et L.M. Gambardella, Ant Colony System : A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evolutionary Computation, volume 1, numéro 1, pages 53-66, 2007.
4. D. Martens, M. De Backer, R. Haesen, J. Vanthienen, Evolutionary Computation, volume 11, number 5, pages 651—665, 2007.
5. Gupta, D.K.; Arora, Y.; Singh, U.K.; Gupta, J.P., "Recursive Ant Colony Optimization for estimation of parameters of a function," Recent Advances in Information Technology (RAIT), 2012 1st International Conference on , vol., no., pp.448,454, 15-17 March 2012
6. Jiawei Han and Micheline Kamber, ―DATA MINING concepts and techniques 2011 edition 978-81-312-0535-8.
7. M. Dorigo, V. Maniezzo, et A. Colorni, Ant systemPart B , volume 26, numéro 1, pages 29-41, 2008
8. M. Dorigo, Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italie, 2012.
9. M. Zlochin, M. Birattari, N. Meuleau, et M. Dorigo, Modelbased search for combinatorial optimization: A critical survey, Annals of Operations Research, vol. 131, pp. 373-395, 2004.
10. Roger S Pressman, ‘Software Engineering’, 2000 Edition, Dreamtech Publications.
11. T. Stützle et H.H. Hoos, MAX MIN Ant System, Future Generation Computer Systems, volume 16, pages 889-914, 2000.
12. .X Hu, J Zhang, and Y Li (2008). Orthogonal methods based ant colony search for solving continuous optimization problems. Journal of Computer Science and Technology, 23(1), pp.2-18.