Abstract
This paper proposes an indexing-based classification technique to classify text documents. The most important purpose of this paper is to index the reduced feature set of text documents. To reduce the feature set, this paper uses locality preserving index (LPI) and regularized locality preserving indexing (RLPI) techniques. The reduced feature sets are indexed using B-Tree. Further, the indexed terms are matched with class indices to categorize the known text document. To reveal the efficacy of the proposed model, large experimentations are carried out on standard benchmark datasets. The outcome of the paper reveals that the presented work outperforms the existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sebastiani, F.: Machine learning in automated text categorization. ACM computing surveys (CSUR), Vol. 34. (2002) 1–47.
Harish, B. S., Revanasiddappa, M. B., & Kumar, S. A. (2015, December). Symbolic Representation of Text Documents Using Multiple Kernel FCM. In International Conference on Mining Intelligence and Knowledge Exploration (pp. 93–102). Springer International Publishing.
Robinson J. T., The KDB tree: A search structure for large multidimensional dynamic indexes. Proceedings of ACM SIGMOD conference Ann Arbor, MI, pp. 10–18.
Dandamudi S. P and Sorenson P. G., 1985. An empirical performance comparison of some variations of the k-d tree and bd tree. Computer and Information Sciences. Vol. 14, no. 3, pp. 134–158.
Kumar A., 1994. G – tree: A new datastructure for organizing multidimensional data. IEEE transactions on Knowledge and Data Engineering, vol. 6, no. 2, pp. 341–347.
Harish, B. S., Manjunath, S., & Guru, D. S. (2012). Text document classification: an approach based on indexing. International Journal of Data Mining & Knowledge Management Process (IJDKP), 2(1), 43–62.
Punitha P., 2005. IARS: Image Archival and Retrieval Systems. Ph.D. Thesis, University of Mysore.
Harish, B. S., Guru, D. S., Manjunath, S.: Representation and classification of text documents: A brief review. IJCA, Special Issue on RTIPPR, (2010) 110–119.
He, X., Cai, D., Liu, H., Ma, W.Y.: Locality preserving indexing for document representation. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 96–103. ACM (2004).
Cai, D., He, X., Zhang, W.V., Han, J.: Regularized locality preserving indexing via spectral regression. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 741–750. ACM (2007).
Uysal, A. K., & Gunal, S. (2014). The impact of preprocessing on text classification. Information Processing & Management, 50(1), 104–112.
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975).
Choudhary, B., Bhattacharyya, P.: Text clustering using universal networking language representation. In: The Proceedings of Eleventh International World Wide Web Conference, pp. 1–7 (2002).
Hotho, A., Maedche, A., Staab, S.: Ontology-based text document clustering 16, 48–54 (2002).
Cavnar, W.: Using an n-gram-based document representation with a vector processing retrieval model, pp. 269–269. NIST SPECIAL PUBLICATION SP (1995).
Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. JAsIs 41(6), 391–407 (1990).
Bentley J. L., 1975. Multidimensional binary search trees used for associative searching. Communications of ACM, vol. 18, no. 9, pp. 509–517.
Minnie, D., & Srinivasan, S. (2011, December). Intelligent Search Engine algorithms on indexing and searching of text documents using text representation. In Recent Trends in Information Systems (ReTIS), 2011 International Conference on (pp. 121–125). IEEE.
Hotho Andreas, Andreas N¨urnberger, and Gerhard Paaß. A brief survey of text mining. In Ldv Forum, volume 20, pages 19–62, 2005.
Han Eui-Hong Sam, George Karypis, and Vipin Kumar. Text categorization using weight adjusted k-nearest neighbor classification. In Pacific-Asia conference on knowledge discovery and data mining, pages 53–65. Springer, 2001.
Han Eui-Hong Sam and Karypis George. Centroid-based document classification: Analysis and experimental results. In European conference on principles of data mining and knowledge discovery, pages 424–431. Springer, 2000.
Korde Vandana and Mahender C Namrata. Text classification and classifiers: A survey. International Journal of Artificial Intelligence & Applications, 3(2):85, 2012.
20newsgroups: http://people.csail.mit.edu/jrennie/20Newsgroups/.
Reuters-21578: http://www.daviddlewis.com/resources/testcollections/reuters21578.
Isa, D., Lee, L.H., Kallimani, V.P. and Rajkumar, R., 2008. Text document preprocessing with the Bayes formula for classification using the support vector machine. IEEE Transactions on Knowledge and Data engineering, 20(9), pp. 1264–1272.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Maheshan, M.S., Harish, B.S., Revanasiddappa, M.B. (2018). Indexing-Based Classification: An Approach Toward Classifying Text Documents. In: Bhateja, V., Nguyen, B., Nguyen, N., Satapathy, S., Le, DN. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_88
Download citation
DOI: https://doi.org/10.1007/978-981-10-7512-4_88
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7511-7
Online ISBN: 978-981-10-7512-4
eBook Packages: EngineeringEngineering (R0)