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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Wednesday 18 July 2018

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


Volume 4, Issue 1, September 2013, Pages 182–188

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

Moe Moe Zaw1 and Ei Ei Mon2

1 Faculty of Information and Communication Technology, University of Technology (Yatanarpon Cyber City), Myanmar
2 Faculty of Information and Communication Technology, University of Technology (Yatanarpon Cyber City), Myanmar

Original language: English

Received 5 July 2013

Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


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.

Author Keywords: Web Document Clustering, Cuckoo Search, Levy Flight, Clustering Algorithm, Relevant information.


How to Cite this Article


Moe Moe Zaw and Ei Ei Mon, “Web Document Clustering Using Cuckoo Search Clustering Algorithm based on Levy Flight,” International Journal of Innovation and Applied Studies, vol. 4, no. 1, pp. 182–188, September 2013.