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D-Pattern Evolving and Inner Pattern Evolving for High Performance Text Mining

  • B. Vignani
  • Suresh Chandra Satapathy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)

Abstract

Many data mining techniques have been introduced to perform different information tasks to mine useful patterns in text documents. However, the way to use effectively and update discovered patterns is still a research issue, particularly within the domain of text mining . Text mining methods adopt term based approach and phrase based approach. Phrase based approach performs better than the term based as phrases carry more information. In this paper we have tendency to propose a new methodology to enhance the utilization of the effectively discovered patterns by including the process of D-pattern evolving and inner pattern evolving.

Keywords

text mining pattern mining pattern deploying pattern evolving 

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References

  1. 1.
    Aas, K., Eikvil, L.: Text Categorisation: A Survey. Technical Report NR 941, Norwegian Computing Center (1999)Google Scholar
  2. 2.
    Joachims, T.: A Probabilistic Analysis of the Rocchio Algorithm with tfidf for Text Categorization. In: Proc. 14th Int’l Conf. Machine Learning (ICML 1997), pp. 143–151 (1997)Google Scholar
  3. 3.
    Lewis, D.D.: An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task. In: Proc. 15th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 1992), pp. 37–50 (1992)Google Scholar
  4. 4.
    Zhong, N., Li, Y., Wu, S.-T.: Effective pattern discovery for text mining (2010)Google Scholar
  5. 5.
    Li, Y., Zhong, N.: Mining Ontology for Automatically Acquiring Web User Information Needs. IEEE Trans. Knowledge and Data Eng. 18(4), 554–568 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Wu, S.-T., Li, Y., Xu, Y.: Deploying Approaches for Pattern Refinement in Text Mining. In: Proc. IEEE Sixth Int’l Conf. Data Mining (ICDM 2006), pp. 1157–1161 (2006)Google Scholar
  7. 7.
    Wu, S.-T., Li, Y., Xu, Y., Pham, B., Chen, P.: Automatic Pattern- Taxonomy Extraction for Web Mining. In: Proc. IEEE/WIC/ACM Int’l Conf. Web Intelligence (WI 2004), pp. 242–248 (2004)Google Scholar
  8. 8.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley (1999)Google Scholar
  9. 9.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20(3), 273–297 (1995)MATHGoogle Scholar
  10. 10.
    Yan, X., Han, J., Afshar, R.: Clospan: Mining Closed Sequential Patterns in Large Datasets. In: Proc. SIAM International Conf. on Data mining (SDM 2003), pp. 166–177 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dept. of CSEAnil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia

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