Advertisement

Document Classification: An Approach Using Feature Clustering

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 235)

Abstract

In this paper, we propose a new method of representing text documents based on feature clustering approach. The proposed representation method is very powerful in reducing the dimensionality of feature vectors for text classification. Further, the proposed method is used to form a symbolic representation (interval valued representation) for text documents. To corroborate the efficacy of the proposed model, we conducted extensive experimentation on standard text datasets. We have compared our classification accuracy achieved by the symbolic classifier with the other existing classifiers like: Naïve Bayes, k-NN, Centroid based and SVM classifiers. The experimental results reveal that the achieved classification accuracy is better than that of the existing methods. In addition our method is based on a simple matching scheme; it requires negligible time for classification.

Keywords

Documents Symbolic Representation Features Clustering Classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Seabastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34, 1–47 (2002)CrossRefGoogle Scholar
  2. 2.
    Jiang, S., Pang, G., Wu, M., Kuang, L.: An improved K-nearest-neighbor algorithm for text categorization. Journal of Expert Systems with Applications 39, 1503–1509 (2012)CrossRefGoogle Scholar
  3. 3.
    Guru, D.S., Harish, B.S., Manjunath, S.: Symbolic representation of text documents. In: Proceedings of Third Annual ACM Compute, Bangalore (2010)Google Scholar
  4. 4.
    Li, Y.H., Jain, A.K.: Classification of Text Documents. The Computer Journal 41, 537–546 (1998)CrossRefMATHGoogle Scholar
  5. 5.
    Hotho, A., Nürnberger, A., Paaß, G.: A Brief Survey of Text Mining. Journal for Computational Linguistics and Language Technology 20, 19–62 (2005)Google Scholar
  6. 6.
    Cavnar, W.B.: Using an N-Gram based document representation with a vector processing retrieval model. In: Third Text Retrieval Conference (TREC-3), pp. 269–278 (1994)Google Scholar
  7. 7.
    Milios, E., Zhang, Y., He, B., Dong, L.: Automatic term extraction and document similarity in special text corpora. In: Sixth Conference of the Pacific Association for Computational Linguistics (PACLing 2003), Canada, pp. 275–284 (2003)Google Scholar
  8. 8.
    Choudhary, B., Bhattacharyya, P.: Text clustering using Universal Networking Language representation. In: Proceedings of Eleventh International World Wide Web Conference (2002)Google Scholar
  9. 9.
    Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T.M., Nigam, K., Slattery, S.: Learning to Extract Symbolic Knowledge from the World Wide Web. In: Proceedings of AAAI/IAAI, pp. 509–516 (1998)Google Scholar
  10. 10.
    Esteban, M., Rodrıguez, O.R.: A Symbolic Representation for Distributed Web Document Clustering. In: Proceedings of Fourth Latin American Web Congress, Cholula, Mexico (2006)Google Scholar
  11. 11.
    Isa, D., Lee, L.H., Kallimani, V.P., Rajkumar, R.: Text document preprocessing with the Bayes formula for classification using the support vector machine. IEEE Transactions on Knowledge and Data Engineering 20, 23–31 (2008)CrossRefGoogle Scholar
  12. 12.
    Wan, C.H., Lee, L.H., Rajkumar, R., Isa, D.: A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine. Journal of American Society of Information Science 41(16), 391–407 (1990)Google Scholar
  13. 13.
    Salton, G., Wang, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18, 613–620 (1975)CrossRefMATHGoogle Scholar
  14. 14.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by Latent Semantic Analysis. Journal of the Expert Systems with Applications 39(15), 11880–11888 (2012)CrossRefGoogle Scholar
  15. 15.
    He, X., Cai, D., Liu, H., Ma, W.Y.: Locality Preserving Indexing for document representation. In: Proceedings of International Conference on Research and Development I Information Retrieval (SIGIR 2004), UK, pp. 96–103 (2004)Google Scholar
  16. 16.
    Cai, D., He, X., Zhang, W.V., Han, J.: Regularized Locality Preserving Indexing via Spectral Regression. In: Proceedings of Conference on Information and Knowledge Management (CIKM 2007), pp. 741–750 (2007)Google Scholar
  17. 17.
    Kyriakopoulou, A., Kalamboukis, T.: Text classification using clustering. In: Proceedings of ECML-PKDD Discovery Challenge Workshop (2006)Google Scholar
  18. 18.
    Pereira, F., Tishby, N., Lee, L.: Distributional clustering of English words. In: Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pp. 183–190 (1993)Google Scholar
  19. 19.
    Slonim, N., Tishby: The power of word clustering for text classification. In: Proceedings of the European Colloquium on IR Research, ECIR 2001 (2001)Google Scholar
  20. 20.
    Dhillon, I., Mallela, S., Kumar, R.: Enhanced word clustering for hierarchical text classification. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Canada, pp. 191–200 (2002)Google Scholar
  21. 21.
    Takamura, H., Matsumoto, Y.: Two-dimensional clustering for text categorization. In: Proceedings of the Sixth Conference on Natural Language Learning (CoNLL 2002), Taiwan, pp. 29–35 (2002)Google Scholar
  22. 22.
    Raskutti, B., Ferr, H., Kowalczyk, A.: Using unlabeled data for text classification through addition of cluster parameters. In: Proceedings of the 19th International Conference on Machine Learning ICML, Australia, pp. 514–521 (2002)Google Scholar
  23. 23.
    Zeng, H.J., Wang, X.H., Chen, Z., Lu, H., Ma, W.Y.: CBC: Clustering based text classification requiring minimal labeled data. In: Proceedings of the 3rd IEEE International Conference on Data Mining, USA, pp. 443–450 (2003)Google Scholar
  24. 24.
    Jiang, J.Y., Liou, R.J., Lee, S.J.: A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification. IEEE Transactions on Knowledge and Data Engineering 23, 335–349 (2011)CrossRefGoogle Scholar
  25. 25.
    Yang, Y., Pedersen, J.P.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 412–420 (1997)Google Scholar
  26. 26.
    Guru, D.S., Nagendraswamy, H.S.: Symbolic Representation of Two-Dimensional Shapes. Pattern Recognition Letters 28, 144–155 (2006)CrossRefGoogle Scholar
  27. 27.
    Bock, H.H., Diday, E.: Analysis of symbolic Data. Springer (1999)Google Scholar
  28. 28.
    Billard, L., Diday, E.: From the statistics of data to the statistics of knowledge: Symbolic data analysis. J. American Statistics Association 98(462), 470–487 (2003)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information Science & EngineeringS.J. College of EngineeringMysoreIndia
  2. 2.Department of Computer Science and EngineeringVidyavardhaka College of EngineeringMysoreIndia

Personalised recommendations