Effective Pattern Discovery and Dimensionality Reduction for Text Under Text Mining

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)

Abstract

Huge data mining techniques have been used for mining useful pattern in text document. Text mining can be used to extract the data in document. It is effectively use and update the discovered pattern; still the research is not yet completed. The existing approach is term-based approach; they suffer the problem of polysemy and synonymy. In the past years, people have used pattern-based approaches for hypothesis, which perform better than the term-based ones, but many of the experiments do not support this hypothesis. This paper presents a new idea about the effective pattern discovery technique which involved the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and useful information.

Keywords

Text mining Polysemy RefixSpan FP-tree SPADE SLPmine GST 

References

  1. 1.
    N. Zhong, Y. Li, S. T. Wu, Effective Pattern Discovery for Text Mining. 24 (2012)Google Scholar
  2. 2.
    K. Aas, Text categorisation: a survey. J. Mach. Learn. Res. 3, 1289–1305 (1999)Google Scholar
  3. 3.
    N. Cancedda, N. Cesa-Bianchi, A. Conconi, C. Gentile, Kernel Methods for Document Filtering. TREC. (2002) Google Scholar
  4. 4.
    J. Han, J. Pei, Y. Yin, Y, Mining Frequent Patterns without Candidate Generation, in.Proceedings ACM SIGMOD Intl Conference Management of Data (SIGMOD 2000). (2000), pp. 1–12Google Scholar
  5. 5.
    Y. Huang, S. Lin, Mining Sequential Patterns Using Graph Search Techniques, in Proceedings 27th Ann. Intl Computer Software and Applications Conference (2003), pp. 4–9Google Scholar
  6. 6.
    N. Jindal, B. Liu, Identifying Comparative Sentences in Text Documents, in Proceedings 29th Ann. Intl ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 2006). (2006), pp. 244–251Google Scholar
  7. 7.
    Y. Li, X. Zhou, P. Bruza, Y. Xu, R. Y. Lau, A Two-Stage Text Mining Model for Information Filtering, in Proceedings ACM 17th Conference Information and Knowledge Management (CIKM 2008). (2008), pp. 1023–1032Google Scholar
  8. 8.
    G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. Intl. J. 24(5), 513–523 (1988)Google Scholar
  9. 9.
    M. Sassano, Virtual Examples for Text Classification with Support Vector Machines, in Proceedings Conference Empirical Methods in Natural Language Processing (EMNLP 2003) (2003), pp. 208–215Google Scholar
  10. 10.
    F. Sebastiani, Machine Learning in Automated Text Categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Information TechnologyBannari Amman Institute of TechnologySathyamangalam, ErodeIndia

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