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Classification of Documents using Effective Pattern Taxonomy
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 86 - Number 6 |
| Year of Publication: 2014 |
| Authors: Mallareddy Uday Kiran, R Ravikanth |
10.5120/14989-2566
|
Mallareddy Uday Kiran, R Ravikanth . Classification of Documents using Effective Pattern Taxonomy. International Journal of Computer Applications. 86, 6 ( January 2014), 19-23. DOI=10.5120/14989-2566
Abstract
Text mining is a technique helps users in extracting useful information from large amount of database available digitally on web or text data. Pattern Taxonomy based model containing sequential pattern used to perform the task. EPT (Effective Pattern Taxonomy) method helps in extracting useful patterns in the text documents by classifying them in Positive and Negative documents. Pattern-based method outperforms keyword based methods. Pattern based method is best way to eliminate meaningless as well as closed sequential patterns thus saving computational time and increases effectiveness of the system.
References
- Ning Zhong, Yuefeng Li, and Sheng-Tang Wu "Effective Pattern Discovery for Text Mining" in IEEE transaction, vol. 24, January 2012.
- Shady Shehata, Member, IEEE, Fakhri Karray, Senior Member, IEEE, and Mohamed S. Kamel, Fellow, IEEE "An Efficient Concept-Based Mining Model for Enhancing Text Clustering" IEEE transactions on knowledge and data engineering, vol. 22, no. 10, October 2010.
- Kavitha Murugeshan, Neeraj RK "Discovering Patterns to Produce Effective Output through Text Mining Using Naïve Bayesian Algorithm" IJITEE ISSN: 2278-3075, Volume-2, Issue-6, May 2013.
- Yuefeng Li Abdulmohsen Algarni Ning Zhong "Mining Positive and Negative Patterns for Relevance Feature Discovery".
- Nikky Rai, Susheel Jain, Anurag Jain "Mining Positive And Negative Association Rule From Frequent And Infrequent Pattern Based On Imlms_Ga" IJCA (0975 – 8887)
- Abdulmohsen Algarni, Yuefeng Li, Xiaohui Tao,"Mining Specific and General Features in Both Positive and Negative Relevance Feedback".
- T. Joachims. "A probabilistic analysis of the rocchio algorithm with tfidf for text categorization". In Proc. Of ICML'97, pages 143–151, 1997.
- S. Shehata, F. Karray, and M. Kamel. "A concept-based model for enhancing text categorization". In Proc. Of KDD'07, pages 629–637, 2007.
- J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation",Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '00), pp. 1-12, 2000.
- S. Scott and S. Matwin, "Feature Engineering for Text Classification," Proc. 16th Int'l Conf. Machine Learning (ICML '99), pp. 379- 388, 1999.
- "Automatic Pattern-Taxonomy Extraction for Web Mining" Sheng-Tang Wu Yuefeng Li Yue Xu Binh Pham Phoebe Chen* IEEE Conference.
- R. Agrawal, and R. Srikant, "Mining sequential patterns," Proceedings of Int. Conf. on Data engineering (ICDE'95), Taipei, Taiwan, 1995, pp. 3-14.
- G. Chang, M. J. Healey, J. A. M. McHugh, and J. T. L. Wang, "Mining the World Wide Web: an information search approach", Kluwer Academic Publishers, 2001, pp. 192.
- D. A. Grossman and O. Frieder, "Information retrieval algorithms and heuristics", Kluwer Academic publishers, Boston, 1998.
- J. D. Holt and S. M. Chung, "Multipass algorithms for mining association rules in text databases", Knowledge and Information Systems vol. 3, 2001, pp. 168-183.
- B. Liu, Y. Dai, X. Li, W. S. Lee, and P. S. Yu, "Building text classifiers using positive and unlabeled examples," ICDM03, 2003, pp. 179- 186.
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