The week's pick
Random Articles
Reseach Article
An Enhancement of Clustering Technique using Support Vector Machine Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 117 - Number 2 |
| Year of Publication: 2015 |
| Authors: Mehajabi Sayeeda, Rachana Kamble |
10.5120/20526-2863
|
Mehajabi Sayeeda, Rachana Kamble . An Enhancement of Clustering Technique using Support Vector Machine Classifier. International Journal of Computer Applications. 117, 2 ( May 2015), 17-22. DOI=10.5120/20526-2863
Abstract
Web surfing is very essential task of daily life for any professional person they search information regarding their field. But to get exact required information from ocean internet of data have become complex task. To manage files and information properly document clustering is a good approach. Clustering method divides text information into subgroup on basis of content based similarity. Document clustering reduces searching effort and fulfils human interest information looking for. It groups similar files together to minimize the search time and complexity. This paper gives new clustering method based on hybrid XNOR function to find degree of similarities within any two documents. Resultant similarity used for document clustering by applying SVM classifier for learning network. This paper introduces new method for document clustering by use of similarity matrix calculation and this matrix is passed for training SVM network for upcoming document classification. The results show the effectiveness of proposed work. In this paper, we describe the formatting guidelines for IJCA Journal Submission.
References
- Janruang, J. , Guha, S. : Semantic suffix tree clustering. In: First IRAST International Conference on Data Engineering and Internet Technology, DEIT (2011)
- Muhammad Rafi, Mehdi Maujood ,Murtaza Munawar Fazal, Syed Muhammad Ali, "A comparison of two suffix tree-based document clustering algorithms", IEEE, 2010.
- Hammouda, K. , Kamel, M. : "Efficient document indexing for web document clustering". IEEE Transactions on Knowledge and Data Engineering 16(10), 1279–1296 (2004)
- Huang, A. : "Similarity measures for text document clustering", pp. 49–56 (2008)
- Hammouda, K. , Kamel, M. : "Phrase-based document similarity based on an index graph model". In: Proceedings of 2002 IEEE International Conference on Data Mining ICDM, pp. 203–210 (2002)
- Jung-Yi Jiang et. al A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 3, MARCH 2011
- Crabtree, D. , Gao, X. , Andreae, P. : "Improving web clustering by cluster selection". In: Proceedings of 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 172–178 (2005)
- Chim, H. , Deng, X. : "A new suffix tree similarity measure for document clustering". In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 121–130. ACM, New York (2007)
- Chim, H. , Deng, X. : "Efficient phrase-based document similarity for clustering". IEEE Transactions on Knowledge and Data Engineering 20(9), 1217–1229 (2008)
- Joydeep, A. S. , Strehl, E. , Ghosh, J. , Mooney, R. : "Impact of similarity measures on web-page clustering". In: Workshop on Artificial Intelligence for Web Search, AAAI, pp. 58–64 (2000)
- Carpineto, C. , Osinski, S. , Romano, G. , Weiss, D. : A survey of web clustering engines. ACM Computing Surveys 41, 1–38 (2009)
- Zamir, O. , Etzioni, O. : Grouper: A dynamic clustering interface to web search results. In: Proceedings of the Eighth International World Wide Web Conference, pp. 283–296. Elsevier, Toronto (1999)
- R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in very large databases, Proceedingsof the ACM SIGMOD Conference on Management of data, 1993, pp. 207–216
- Congnan Luo, , Yanjun Li, Soon M. Chung. Text document clustering based on neighbors , Data & Knowledge Engineering (68), 2009,1271–1288
- Tianming Hu,Sam Yuan Sung, Hui Xiong, Qian Fu. Discovery of maximum length frequent itemsets, Information Sciences (178), 2008,69–87
- Wen Zhanga,, Taketoshi Yoshida, Xijin Tang, Qing Wang. Text clustering using frequent itemsets, Knowledge-Based Systems 23 (2010) 379–388
- Radhakrishna. V,C. Srinivas, C. V. Guru rao. High Performance Pattern Search algorithm using three sliding windows, International Journal of Computer Engineering and Technology , Volume 3,issue 2, 2012 , pages 543-552. Impact factor 3. 85.
- Ng, A. Y. , Jordan, M. I. , Weiss, Y. : On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press (2001)
- Beil, F. , Ester, M. , Xu, X. : Frequent term-based text clustering. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 436–442. ACM, New York (2002)
- Cilibrasi, R. , Vitanyi, P. : The google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)
- Kjos-hanssen, B. , Evangelista, A. J. : Google distance between words. Computing Research Repository abs/0901. 4 (2009).
- Rachana Kamble, Mehajabi Sayeeda, Clustering Software Methods and Comparison, Volume 5 Issue 6, Pages 1878-1885 IJCTA November-December 2014.
Index Terms
Keywords