Proficient Model of Inventive Approach for Content Mining

Authors

  • M Mahesh Babu  Department of Computer Science and Engineering, Seshachala Institute of Technology, Puttur, Andhra Pradesh, India
  • B Srinivasulu  Professor, Department of Computer Science and Engineering, Seshachala Institute of Technology, Puttur, Andhra Pradesh, India

Keywords:

Text mining, text classification, pattern mining, pattern evolving, information filtering.

Abstract

The continued growth in information entails the requirement for the mixing of structured information with the goal of creating information accessible from numerous independent and heterogeneous sources. Due to the rising of digital information created accessible in recent years, knowledge discovery and data processing have attracted an excellent deal of attention with an imminent want for turning such information into helpful information and knowledge. Several data mining techniques are proposed for mining helpful patterns in text documents. However, how to effectively use and update discovered patterns continues to be an open analysis issue, particularly within the domain of text mining. Since most existing text mining strategies adopted term-based approaches, all of them suffer from the issues of lexical ambiguity and synonymy. Over the years, people have typically command the hypothesis that pattern (or phrase)-based approaches ought to perform higher than the term-based ones, however several experiments don't support this hypothesis. This paper presents an innovative and effective pattern discovery technique which incorporates the processes of pattern deploying and pattern evolving, to boost the effectiveness of using and change discovered patterns for locating relevant and fascinating information. Substantial experiments on RCV1 information assortment and TREC topics demonstrate that the proposed solution achieves encouraging performance.

References

  1. K. Aas and L. Eikvil, "Text Categorisation: A Survey," Technical Report Raport NR 941, Norwegian Computing Center, 1999.
  2. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases," Proc. 20th Int'l Conf. Very Large Data Bases (VLDB '94), pp. 478-499, 1994.
  3. H. Ahonen, O. Heinonen, M. Klemettinen, and A.I. Verkamo, "Applying Data Mining Techniques for Descriptive Phrase Extraction in Digital Document Collections," Proc. IEEE Int'l Forum on Research and Technology Advances in Digital Libraries (ADL '98), pp. 2-11, 1998.
  4. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison Wesley, 1999.
  5. N. Cancedda, N. Cesa-Bianchi, A. Conconi, and C. Gentile, "Kernel Methods for Document Filtering," TREC, trec.nist.gov/pubs/trec11/papers/kermit.ps.gz, 2002.
  6. N. Cancedda, E. Gaussier, C. Goutte, and J.-M. Renders, "Word- Sequence Kernels," J. Machine Learning Research, vol. 3, pp. 1059- 1082, 2003.
  7. M.F. Caropreso, S. Matwin, and F. Sebastiani, "Statistical Phrases in Automated Text Categorization," Technical Report IEI-B4-07- 2000, Instituto di Elaborazione dell'Informazione, 2000.
  8. C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
  9. S.T. Dumais, "Improving the Retrieval of Information from External Sources," Behavior Research Methods, Instruments, and Computers, vol. 23, no. 2, pp. 229-236, 1991.
  10. J. Han and K.C.-C. Chang, "Data Mining for Web Intelligence," Computer, vol. 35, no. 11, pp. 64-70, Nov. 2002.
  11. 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.
  12. Y. Huang and S. Lin, "Mining Sequential Patterns Using Graph Search Techniques," Proc. 27th Ann. Int'l Computer Software and Applications Conf., pp. 4-9, 2003.
  13. N. Jindal and B. Liu, "Identifying Comparative Sentences in Text Documents," Proc. 29th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '06), pp. 244-251, 2006.
  14. T. Joachims, "A Probabilistic Analysis of the Rocchio Algorithm with tfidf for Text Categorization," Proc. 14th Int'l Conf. Machine Learning (ICML '97), pp. 143-151, 1997.
  15. T. Joachims, "Text Categorization with Support Vector Machines: Learning with Many Relevant Features," Proc. European Conf. Machine Learning (ICML '98),, pp. 137-142, 1998.
  16. T. Joachims, "Transductive Inference for Text Classification Using Support Vector Machines," Proc. 16th Int'l Conf. Machine Learning (ICML '99), pp. 200-209, 1999.
  17. W. Lam, M.E. Ruiz, and P. Srinivasan, "Automatic Text Categorization and Its Application to Text Retrieval," IEEE Trans. Knowledge and Data Eng., vol. 11, no. 6, pp. 865-879, Nov./Dec. 1999.
  18. D.D. Lewis, "An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task," Proc. 15th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '92), pp. 37-50, 1992.
  19. D.D. Lewis, "Feature Selection and Feature Extraction for Text Categorization," Proc. Workshop Speech and Natural Language, pp. 212-217, 1992.
  20. D.D. Lewis, "Evaluating and Optimizing Automous Text ClassificationSystems," Proc. 18th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '95), pp. 246-254, 1995.
  21. X. Li and B. Liu, "Learning to Classify Texts Using Positive and Unlabeled Data," Proc. Int'l Joint Conf. Artificial Intelligence (IJCAI '03), pp. 587-594, 2003.
  22. Y. Li, W. Yang, and Y. Xu, "Multi-Tier Granule Mining for Representations of Multidimensional Association Rules," Proc.IEEE Sixth Int'l Conf. Data Mining (ICDM '06), pp. 953-958, 2006.
  23. Y. Li, C. Zhang, and J.R. Swan, "An Information Filtering Model on the Web and Its Application in Jobagent," Knowledge-Based Systems, vol. 13, no. 5, pp. 285-296, 2000.
  24. Y. Li and N. Zhong, "Interpretations of Association Rules by Granular Computing," Proc. IEEE Third Int'l Conf. Data Mining(ICDM '03), pp. 593-596, 2003.
  25. Y. Li and N. Zhong, "Mining Ontology for Automatically Acquiring Web User Information Needs," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 4, pp. 554-568, Apr. 2006.
  26. Y. Li, X. Zhou, P. Bruza, Y. Xu, and R.Y. Lau, "A Two-Stage Text Mining Model for Information Filtering," Proc. ACM 17th Conf. Information and Knowledge Management (CIKM '08), pp. 1023-1032, 2008.
  27. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins, "Text Classification Using String Kernels," J. Machine Learning Research, vol. 2, pp. 419-444, 2002.
  28. A. Maedche, Ontology Learning for the Semantic Web. Kluwer Academic, 2003.
  29. C. Manning and H. Schu¨ tze, Foundations of Statistical Natural Language Processing. MIT Press, 1999.
  30. I. Moulinier, G. Raskinis, and J. Ganascia, "Text Categorization: A Symbolic Approach," Proc. Fifth Ann. Symp. Document Analysis and Information Retrieval (SDAIR), pp. 87-99, 1996.
  31. J.S. Park, M.S. Chen, and P.S. Yu, "An Effective Hash-Based Algorithm for Mining Association Rules," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '95), pp. 175-186, 1995.
  32. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, "Prefixspan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth," Proc. 17th Int'l Conf. Data Eng.(ICDE '01), pp. 215-224, 2001.
  33. M.F. Porter, "An Algorithm for Suffix Stripping," Program, vol. 14, no. 3, pp. 130-137, 1980.
  34. S. Robertson and I. Soboroff, "The Trec 2002 Filtering Track Report," TREC, 2002, trec.nist.gov/pubs/trec11/papers/OVER. FILTERING.ps.gz.
  35. S.E. Robertson, S. Walker, and M. Hancock-Beaulieu, "Experimentation as a Way of Life: Okapi at Trec," Information Processing and Management, vol. 36, no. 1, pp. 95-108, 2000.
  36. J. Rocchio, Relevance Feedback in Information Retrieval. chapter 14, Prentice-Hall, pp. 313-323, 1971.
  37. T. Rose, M. Stevenson, and M. Whitehead, "The Reuters Corpus Volume1—From Yesterday's News to Today's Language Resources," Proc. Third Int'l Conf. Language Resources and Evaluation, pp. 29-31, 2002.
  38. G. Salton and C. Buckley, "Term-Weighting Approaches in Automatic Text Retrieval," Information Processing and Management: An Int'l J., vol. 24, no. 5, pp. 513-523, 1988.
  39. M. Sassano, "Virtual Examples for Text Classification with Support Vector Machines," Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP '03), pp. 208-215, 2003.
  40. S. Scott and S. Matwin, "Feature Engineering for Text Classification," Proc. 16th Int'l Conf. Machine Learning (ICML '99), pp. 379- 388, 1999.
  41. F. Sebastiani, "Machine Learning in Automated Text Categorization,"ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
M Mahesh Babu, B Srinivasulu, " Proficient Model of Inventive Approach for Content Mining, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1471-1476, January-February-2018.