Abstract
This paper presents new approach analytical results of document clustering for vectors. The proposed analytical results of document clustering for vectors approach is based on mean clusters. In this paper we have used six iterations \(\text {I}_{1}\) to \(\text {I}_{6}\) for document clustering results. The steps Document collection, Text Pre-processing, Feature Selection, Indexing, Clustering Process and Results Analysis are used. Twenty news group data sets are used in the experiments. The experimental results are evaluated using the numerical computing MATLAB 7.14 software. The experimental results show the proposed approach out performs.
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
Arora, S., Raghavan, P., Rao, S.: Approximation schemes for Euclidean k-median and related problems. In: Proceedings of the 30th Annual ACM Symposium on Theory of Computing, May 1998, pp. 106–113
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Comm. ACM 18, 509–517 (1975)
Bradley, P.S., Fayyad, U.: Refining initial points for k-means clustering. In: Proceedings of the 15th International Conference on Machine Learning, 1998, pp. 91–99
Dasgupta, S.: Learning mixtures of Gaussians. In: Proceedings of the 40th IEEE Symposium on Foundations of Computer Science, Oct 1999, pp. 634–644
Du, Q., Faber, V., Gunzburger, M.: Centroidal Voronoi tesselations: Applications and algorithms. SIAM Rev. 41, 637–676 (1999)
Agarwal, P.K., Procopiuc, C.M.: Exact and approximation algorithms for clustering. In: Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, Jan 1998, pp. 658–667
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Faber, V.: Clustering and the continuous k-means algorithm. Los Alamos Sci. 22, 138–144 (1994)
Arya, S., Mount, D.M.: Approximate range searching. Comput. Geom. Theor. Appl. 17, 135–163 (2000)
Forgey, E.: Cluster analysis of multivariate data: efficiency versus interpretability of classification. Biometrics 21, 768 (1965)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Cambridge (1996)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, Boston (1990)
Ball, G.H., Hall, D.J.: Some fundamental concepts and synthesis procedures for pattern recognition preprocessors. In: Proceedings of the International Conference on Microwaves, Circuit Theory, and Information Theory, Sept 1964
Dasgupta, S., Shulman, L.J.: A two-round variant of EM for Gaussian mixtures. In: Procedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI-2000), June 2000, pp. 152–159
Feller, W.: An Introduction to Probability Theory and Its Applications, 3rd edn. Wiley, New York (1968)
Alsabti, K., Ranka, S., Singh, V.: An Efficient k-means clustering algorithm. In: Proceedings of the First Workshop High Performance Data Mining, Mar 1998
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching. J. ACM 45, 891–923 (1998)
Bottou, L., Bengio, Y.: Convergence properties of the k-means algorithms. In: Tesauro, G., Touretzky, D. (eds.) Advances in Neural Information Processing Systems 7, pp. 585–592. MIT Press, Cambridge (1996)
Coggins, J.M., Jain, A.K.: A spatial filtering approach to texture analysis. Pattern Recognit. Lett. 3, 195–203 (1985)
Ester, M., Kriegel, H., Xu, X.: A database interface for clustering in large spatial databases. In: Procedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995, pp. 94–99
Neeraj, S., Thakur, G.S. : Hesitant distance similarity measures for document clustering. In: IEEE Conference—2011 World Congress on Information and Communication Technologies Mumbai, 11–14 Dec 2011. ISBN: 978-1-4673-0125-1
Sahu, S.K., Sahu, N., Thakur, G.S.: Classification of document clustering approaches. Intl. J. Comput. Sci. Softw. Eng. 2(5), 509–513 (2012). ISSN (Online): 2277 128X
Sahu, B., Sahu, N., Thakur, G.S.: Architecture based users and administrator login data processing. In: International Conference on Intelligent Computing and Information System (ICICIS-2012), Pachmarhi, Piparia, 27–28 Oct 2012. ISSN (Online): 2249–071X
Bradley, P.S., Fayyad, U., Reina, C.: Scaling clustering algorithms to large databases. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 1998, pp. 9–15
Acknowledgments
This work is supported by research grant from MPCST, Bhopal M.P., India, Endt.No. 2427/CST/R&D/2011 dated 22/09/2011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Sahu, N., Thakur, G.S. (2014). Computing Vectors Based Document Clustering and Numerical Result Analysis. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_138
Download citation
DOI: https://doi.org/10.1007/978-81-322-1602-5_138
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1601-8
Online ISBN: 978-81-322-1602-5
eBook Packages: EngineeringEngineering (R0)