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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 8, August 2011
Web Objects Clustering Through Aggregation for Enhanced Search Results
Full Text(PDF, 3000)  PP.  
Dr. Pushpa R. Suri and Harmunish Taneja
Clustering, Information Retrieval (IR), Inheritance, Object Oriented, Search Engine, Web objects, World Wide Web (WWW).
World Wide Web offer a rich mix of new challenges and opportunities to information computing researchers. The conventional search engine always returns a set of web pages in answer to a user query. Millions of web pages from organizations, institutions and personnel are made public electronically. With the web explosion and never ending raise of digital data, an added effect is the difficulty to retrieve relevant and reliable information from the Web. It is almost impossible for the naive user to get the right information in the answered search results as there is too much unrelated and out dated. The reason for this is rooted deep in the methodology for conventional Information computing on web that supports the indexing granularity for search as a web page. Search engines basically hunt for the potential web pages of user interests. On the contrary the user perspective in today's changing era is the information of a certain 'object' may be in the form of a cluster containing only the relevant data related to the object of interest rather than a tedious list of search results containing all the related and unrelated web pages. The similar theory can be applied to the queries from the point of view of developer. It requires grouping web objects into classes based on their attributes and links. This paper proposes algorithm for clustering web objects into different classes based on their links and identify relations dynamically. Results confirm the efficiency of the proposed approach as the user gets a cluster that contains only objects of interest from all the linked web pages.
[1] S. Lawrence, and G.L. Giles, “Context and Page Analysis for Improved Web search”, IEEE Internet Computing, vol. 2, pp. 38- 46, 1998. (Journal citation)

[2] Han J., and Kamber M., Data Mining: Concepts and Techniques, 2nd Ed., San Francisco: The Morgan Kaufmann Publishers, 2006. (Book style)

[3] Jia Rongfei, Jin Maozhong, and Wang Xiaobo, “Web Objects Clustering Using Transaction Log,” Proc. Third International Conference on Knowledge Discovery and Data Mining by IEEE Computer Society, pp. 182-186, 2010. (Conference Peoceedings)

[4] W. Chan, W. Leung, and D. Lee, “Clustering Search Engine Query Log Containing Noisy Clickthroughs,” Proc. SAINT Conference, Tokyo, Japan, pp. 305-308, 2004. (Conference Peoceedings)

[5] R. Xu, and I. Donald Wunsch, “Survey of Clustering Algorithms,” IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 645, 2005. (IEEE Transactions)

[6] A. Strehl, J. Ghosh, and R. Mooney, “Impact of Similarity Measures on Web-page Clustering,” Proc. AAAI Workshop on AI for Web Search (AAAI 2000), Austin, pp. 58–64, 2000 (Conference Peoceedings)

[7] S. Brin and L. Page, “The Anatomy of a Large-scale Hypertextual Web Search Engine,” Computer networks and ISDN systems, vol. 30, no. 1-7, pp. 107–117, 1998. (Journal citation)

[8] D. Beeferman, and A. Berger, “Agglomerative Clustering of a Search Engine Query Log,” Proc. of the Sixth ACM SIGKDD International conference on Knowledge discovery and data mining, ACM New York, NY, USA, pp. 407–416, 2000. (Conference Peoceedings)

[9] D. Boley, M. Gini, R. Gross, E.H. Han, K. Hastings, G. Karypis, V. Kumar, B. Mobasher, and J. Moore, ""Partitioning-based Clustering for Web Document Categorization,"" Decision Support Systems, vol. 27, pp. 329-341, 1999. (Journal citation)

[10] O. Zamir and O. Etzioni, ""Web Document Clustering: A Feasibility Demonstration,"" Proc. of SIGIR '98, pp. 46--53, 1998. (Conference Peoceedings)

[11] A. Strehl, J. Ghosh, and R. Mooney, ""Impact of Similarity Measures on Web-page Clustering,"" Proc. of the AAAI 2000 Workshop on Artificial Intelligence for WebSearch, pp. 58--64, Austin, Texas, July 2000. (Conference Peoceedings)

[12] Y. Fu, K. Sandhu, and M. Shih, ""Clustering of Web Users Based on Access Patterns,"" Proc. of the 1999 KDD Workshop on Web Mining, San Diego, Canada, 1999. (Conference Peoceedings)

[13] J. Wen, J.Y. Nie, H. Zhang, ""Query Clustering Using User Logs,"" ACM Transactions on Information Systems, vol. 20, no. 1, pp. 59- 81, 2002. (ACM Transactions)

[14] Z. Su, Q. Yang, H. J. Zhang, X. Xu and Y. H. Hu, ""Correlationbased Document Clustering using Web Logs,"" Proc. of the 34th Hawaii International Conference On System Sciences (HICSS-34), January 3-6, pp. 1-7, 2001. (Conference Peoceedings)

[15] Sanjeev Sharma, R.K. Gupta, “Improved BSP Clustering Algorithm For Social Network Analysis,” International Journal of Grid and Distributed Computing, vol. 2, no. 3, pp. 67-76, Sept. 2010. (Journal citation)

[16] Deepti Gupta, Komal Kumar Bhatia, and A.K. Sharma, “A Novel Indexing Technique for Web Documents using Hierarchical Clustering,” IJCSNS International Journal of Computer Science and Network Security, vol.9 no.9, pp. 168-175, , September 2009. (Journal citation)

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