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Document Classification: An Approach Using Feature Clustering

  • Conference paper
Recent Advances in Intelligent Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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References

  1. Seabastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34, 1–47 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Li, Y.H., Jain, A.K.: Classification of Text Documents. The Computer Journal 41, 537–546 (1998)

    Article  MATH  Google Scholar 

  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. 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. 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. Choudhary, B., Bhattacharyya, P.: Text clustering using Universal Networking Language representation. In: Proceedings of Eleventh International World Wide Web Conference (2002)

    Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Salton, G., Wang, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18, 613–620 (1975)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Kyriakopoulou, A., Kalamboukis, T.: Text classification using clustering. In: Proceedings of ECML-PKDD Discovery Challenge Workshop (2006)

    Google Scholar 

  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. 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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  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. Guru, D.S., Nagendraswamy, H.S.: Symbolic Representation of Two-Dimensional Shapes. Pattern Recognition Letters 28, 144–155 (2006)

    Article  Google Scholar 

  27. Bock, H.H., Diday, E.: Analysis of symbolic Data. Springer (1999)

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Science & Engineering, S.J. College of Engineering, Mysore, 570 006, Karnataka, India

    B. S. Harish

  2. Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, 570 002, Karnataka, India

    B. Udayasri

Authors
  1. B. S. Harish
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  2. B. Udayasri
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Corresponding author

Correspondence to B. S. Harish .

Editor information

Editors and Affiliations

  1. Technopark Campus Trivandrum, Indian Inst. of Information Technology and Management – Kerala (IIITM-K), Kerala, India

    Sabu M. Thampi

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, USA

    Ajith Abraham

  3. Indian Statistical Institute, Kolkata, India

    Sankar Kumar Pal

  4. Department of Computer Science School of Science, University of Salamanca, Salamanca, Spain

    Juan Manuel Corchado Rodriguez

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© 2014 Springer International Publishing Switzerland

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Harish, B.S., Udayasri, B. (2014). Document Classification: An Approach Using Feature Clustering. In: Thampi, S., Abraham, A., Pal, S., Rodriguez, J. (eds) Recent Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 235. Springer, Cham. https://doi.org/10.1007/978-3-319-01778-5_17

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  • DOI: https://doi.org/10.1007/978-3-319-01778-5_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01777-8

  • Online ISBN: 978-3-319-01778-5

  • eBook Packages: EngineeringEngineering (R0)

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