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Reseach Article

A Review of Merging based on Suffix Tree Clustering

Published on December 2015 by Priya S. Pujari, Arti Waghmare
National Conference on Advances in Computing
Foundation of Computer Science USA
NCAC2015 - Number 2
December 2015
Authors: Priya S. Pujari, Arti Waghmare

Priya S. Pujari, Arti Waghmare . A Review of Merging based on Suffix Tree Clustering. National Conference on Advances in Computing. NCAC2015, 2 (December 2015), 28-31.

author = { Priya S. Pujari, Arti Waghmare },
title = { A Review of Merging based on Suffix Tree Clustering },
journal = { National Conference on Advances in Computing },
issue_date = { December 2015 },
volume = { NCAC2015 },
number = { 2 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 28-31 },
numpages = 4,
url = { /proceedings/ncac2015/number2/23367-5030/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 National Conference on Advances in Computing
%A Priya S. Pujari
%A Arti Waghmare
%T A Review of Merging based on Suffix Tree Clustering
%J National Conference on Advances in Computing
%@ 0975-8887
%V NCAC2015
%N 2
%P 28-31
%D 2015
%I International Journal of Computer Applications

Now a days web is growing very quickly, therefore it is important to search useful Patterns for web search document. The clustering of web search result has become a very interesting and popular research area to many organizations as it provides useful insights to information retrieval. Clustering of web search result system provides the search result for the user very concise and accurate, also provides reviews on them and locate specific information of interest. Clustering techniques can be used to organize retrieved results into a set of group based on their similarities. Several approaches existed for web document clustering using a suffix tree, but results show there is more research remains to be done. This paper presents a various approaches to Suffix Tree Cluster merge techniques to generate the informative, meaningful cluster and also compare some of the important cluster merging methods according to its performance . The method to merge clusters is used to sort out the problem of merging boundaries. Identical clusters are merged together based on a given estimation criterion until no more clusters can be merged. This survey aims to provide useful guidance for many applications where merging is having a remarkable impact on clustering.

  1. E. Ukkonen, "On-line construction of suffix trees," Algorithmica, vol. 14, 1995, pp 249-260.
  2. S. Osi´nski, J. Stefanowski, and D. Weiss," Lingo: Search results clustering algorithm based on singular value decomposition," In Proceedings of the International IIS: Intelligent Information Processing and Web Mining Conference, Advances in Soft Computing, pages 359–368, Zakopane, Poland, 2004.
  3. S. Khoja , 1999. Stemming Arabic Text
  4. Zhao, P. And Cai, Q. S," Research Of Novel Chinese Text Clustering Algorithm Based On Hownet," In Computer Engineering And Applications, 43, 162-163,2007
  5. Liu B. , Chin C. W. , and Ng, H. T. Mining Topic-Specific Concepts and Definitions on tlie Web. In Proceedings of the Twelfth International World Wide Web Conference (WWW'OS), Budapest, Hungary. 2003
  6. Jianhua Wang, Ruixu Li," A New Cluster Merging Algorithm Of Suffix Tree Clustering," In International Federation For Information Processing, Volume 228, pp. 197-203, 2006
  7. Archana Kale, Ujwala Bharambe, M. Sashikumar," New Suffix Tree Similarity Measure And Labeling For Web Search Results Clustering," In 2nd International Conference On Emerging Trends In Engineering And Technology, pp 856 –861,Dec 2009
  8. Ferragina, P. and Gulli, A. (2004) "The Anatomy of a Hierarchical Clustering Engine for Webpage, News and Book Snippets," Technical report, RR04-04 Informatica, Pisa.
  9. Jongkol Janruang, Worapoj Kreesuradej, "A New Web Search Result Clustering based True Common Phrase Label Discovery," International Conference on Computational Intelligence for Modeling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2006.
  10. A. K. Jain, M. N. Murty, and P. J. Flynn,"Data clustering: A review," ACM Computing Surveys (CSUR), 31(3),pp 264–323,1999
  11. Zamir, O. & Etzioni, O. (1998),"Web Document Clustering: A Feasibility Demonstration," Proc ACMSIGIR'98, Melbourne, Australia, 46-54.
  12. Aditi Chaturvedi, Dr. Kavita Burse, Rachna Mishra," Affinity Propagation Based Document Clustering Using Suffix Tree," In IJERT, Vol. 3, Issue 1, Jan 2014
  13. Hung Chim and Xiao tie Deng," Efficient Phrase-Based Document Similarity for Clustering," in IEEE Transaction on knowledge and data engineering , VOL. 20, NO. 9, Sept 2008
  14. Thaiprayoon S, Kongthon. A , Palingoon P And Haruechaiyasak. C," Search Result Clustering For Thai Twitter Based On Suffix Tree Clustering," In 9th International Conference , pp 1 – 4, May 2012
  15. F. Perez-Tellez and D. Pinto, "Om the Difficult of Clustering Company Tweets," Proc. Of the 2nd International Workshop on Search and Mining User-generated Content (SMUC '10), pp. 95–102, 2010.
  16. A. Rangrej, S. Kulkarni and A. V. Tendulkar, "Comparative Study of Clustering Techniques for Short Text Documents," Proc. Of the 20th International World Wide Web Conference (WWW, '11), pp. 111–112, 2011
  17. Andrea Bernardini, Claudio Carpineto, Massimiliano D'amico," Full-Subtopic Retrieval With Keyphrase-Based Search Results Clustering," In IEEE/WIC/ACM International Conferences On Web Intelligence And Intelligent Agent Technologies-Workshops,Volume 1,Pp 206 – 213,Sept 2009
  18. R. Mahalakshmi, V. Lakshmi Praba," A Relative Study On Search Results Clustering Algorithms - K-Means, Suffix Tree And Lingo," In International Journal Of Engineering And Advanced Technology, Volume-2, Aug 2013.
  19. Oren Zamir ,Oren EtzioniO. "Grouper: A Dynamic Clustering Interface to Web Search Results," University of Washington. Department of Computer Science and Engineering. 1999K. Elissa
  20. Guodong Hu, Wanli Zuo, Fengling He,Ying Wang," Semantic-Based Hierarchicalize The Result Of Suffix Tree Clustering," Proc In Second International Symposium On Knowledge Acquisition And Modeling, Volume 03,pp 221-224 , 2009
  21. Lin, X. , "A self-organizing semantic map for informationretrieval," Proceedings of the 14th International ACM SIGIRConference on Research and Development in Information Retrieval (SIGIR'91), 1991, pp. 262-269.
Index Terms

Computer Science
Information Sciences


Web Information Retrieval Grouping Similarity Search Result Snippets