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Web Document Clustering Using Cuckoo Search Clustering Algorithm based on Levy Flight

Journal Name:

  • International Journal of Innovation and Applied Studies

Publication Year:

  • 2013

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
The World Wide Web serves as a huge widely distributed global information service center. The tremendous amount of information on the web is improving day by day. So, the process of finding the relevant information on the web is a major challenge in Information Retrieval. This leads the need for the development of new techniques for helping users to effectively navigate, summarize and organize the overwhelmed information. One of the techniques that can play an important role towards the achievement of this objective is web document clustering. This paper aims to develop a clustering algorithm and apply in web document clustering area. The Cuckoo Search Optimization algorithm is a recently developed optimization algorithm based on the obligate behavior of some cuckoo species in combining with the levy flight. In this paper, Cuckoo Search Clustering Algorithm based on levy flight is proposed. This algorithm is the application of Cuckoo Search Optimization algorithm in web document clustering area to locate the optimal centroids of the cluster and to find global solution of the clustering algorithm. For testing the performance of the proposed method, this paper will show the experience result by using the benchmark dataset. The result obtained shows that the Cuckoo Search Clustering algorithm based on Levy Flight performs well in web document clustering.
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  • English

REFERENCES

References: 

[1] D. Boley, M. Gini, R. Cross, E. Hong(Sam), K. Hastings, G. Karypis, V. Kumar, B. Mobasher, and J. Moore, “Partitioningbased
Clustering for Web Document Categorization", Decision Support Systems-DSS, vol. 27, pp. 329-341, 1999.
[2] Jain, A.K. and Dubes, R.C, Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs, New Jersey, USA, 1988.
[3] Bottou, L. and Bengio, Y., “Convergence properties of the k-means algorithm”, Advances in Neural Information
Processing Systems, 1995, 7, 585-592.
[4] Xiaohui Cui, Thomas E. Potok, Paul Palathingal, “Document Clustering Using Particle Swarm Optimization”, Swarm
Intelligence Symposium, IEEE publication, 8-10 June 2005.
[5] Jeevan H E, Prashanth P P, Punith Kumar S N, Vinay Hegde, “Web Pages Clustering: A New Approach”, International
Journal Of Innovative Technology & Creative Engineering, ISSN:2045-8711, vol. 1, no. 4, April 2011.
[6] Rajendra Kumar Roul1, Dr.S.K.Sahay, “An Effictive Web Document Clustering For Information Retrieval”, International
Journal of Computer Science and Management Research, vol. 1, no. 3, p. 481, 2012.
[7] Samiksha Goel, Arpita Sharma, Punam Bedi, “Cuckoo Search Clustering Algorithm: A novel strategy of biomimicry”,
World Congress on Information and Communication Technologies, IEEE publication, 2011.
[8] Moe Moe Zaw, Ei Ei Mon, “Improved Cuckoo Search Clustering Algorithm(ICSCA)”, Proceedings of the 11th International
Conference on Computer Applications, pp. 22-26, 2013.
[9] Swapnali Ware, N.A. Dhawas, “Web Document Clustering Using KEA-Means Algorithm”, International Journal Of
Computer Technology & Applications, vol. 3 (5), pp. 1720-1725, 2012.
[10] Xin-She Yang, Suash Deb, “Cuckoo Search via Levy Flights”, World Congress on Nature and Biologically Inspired
Algorithms, IEEE publication, pp. 210-214, 2009.
[11] A. Kaveh, T. Bakhshpoori and M. Ashoory, “An Efficient Optimization Procedure Based On Cuckoo Search Algorithm For
Practical Design of Steel Structures”, International Journal Of Optimization In Civil Engineering, 2012; 2(1):1-14.
[12] Vipinkumar Tiwari, “Face Recognition based on Cuckoo Search Algorithm”, Indian Journal of Computer Science and
Engineering, ISSN : 0976-5166, vol. 3, no. 3, Jun-Jul 2012.
[13] X.-S. Yang and S. Deb, “Engineering Optimisation by Cuckoo Search”, International Journal of Mathematical Modelling
and Numerical Optimisation, vol. 1, no. 4, pp. 330-343, 2010.
[14] X.-S. Yang, Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, 2010.

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