Abstract
The smooth support vector machine (SSVM) is one of the promising algorithms for classification problems. However, it is restricted to work well on a small to moderate dataset. There exist computational difficulties when we use SSVM with non linear kernel to deal with large dataset. Based on SSVM, the reduced support vector machine (RSVM) was proposed to solve these difficulties using a randomly selected subset of data to obtain a nonlinear separating surface. In this paper, we propose an alternative algorithm, k-mode RSVM (KMO-RSVM) that combines RSVM with k-mode clustering technique to handle classification problems on categorical large dataset. In our experiments, we tested the effectiveness of KMO-RSVM on four public available dataset. It turns out that KMO-RSVM can improve speed of running time significantly than SSVM and still obtained a high accuracy. Comparison with RSVM indicates that KMO-RSVM is faster, gets smaller reduced set and comparable testing accuracy than RSVM.
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
Preview
Unable to display preview. Download preview PDF.
References
Chaturvedi, A., Green, P., Carrol, J.: K-Modes Clustering. Journal of Classification 18, 35–55 (2001)
Chien, L.J., Chang, C.C., Lee, Y.J.: Variant Methods of Reduced Set Selection for Reduced Support Vector achines. Journal of Information Science and Engineering 26(1) (2010)
Chang, C.-C., Lee, Y.-J.: Generating the reduced set by systematic sampling. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 720–725. Springer, Heidelberg (2004)
Gowda, K.C., Diday, E.: Symbolic clustering using a new dissimilarity measure. Pattern Recognition Letters 24(6), 567–578 (1991)
He, Z., Xu, X., Deng, S.: A cluster ensemble for clustering categorical data. Information Fusion 6, 143–151 (2005)
Huang, Z.: Clustering large data sets with mixed numeric and categorical values. In: Proceedings of The First Pacific sia Knowledge Discovery and Data Mining Conference, Singapore (1997)
Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining nd Knowledge Discovery 2, 283–304 (1998)
Huang, Z.: A Note on K-modes Clustering. Journal of Classification 20, 257–261 (2003)
Huang, C.M., Lee, Y.J., Lin, D.K.J., Huang, S.Y.: Model Selection for Support Vector Machines via Uniform esign. A Special issue on Machine Learning and Robust Data Mining of Computational Statistics and Data Analysis 52, 335–346 (2007)
Hsu, C.W., Chang, C.C., Lin, C.J.: Practical Guide To Support Vector Classification. Department of Computer cience and Information Engineering National Taiwan University (2003), http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Mardia, K., et al.: Multivariate Analysis. Academic Press, London (1979)
Lee, Y.J.: Support vector machines in data mining. PhD thesis. University of Wisconsin-Madison, USA (2001)
Lee, Y.J., Mangasarian, O.L.: A Smooth Support Vector Machine. J. Comput. Optimiz. Appli. 20, 5–22 (2001)
Lee, Y.J., Mangasarian, O.L.: RSVM: Reduced Support Vector Machines. In: Proceedings of the First SIAM International Conference on Data Mining. SIAM, Philadelphia (2001)
Lee, Y.J., Huang, S.Y.: Reduced Support Vector Machines: A Statistical Theory. IEEE Trans.Neural Network. 18(1) (2007)
Jen, L.-R., Lee, Y.-J.: Clustering model selection for reduced support vector machines. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 714–719. Springer, Heidelberg (2004)
Lin, K.M., Lin, C.J.: A Study on Reduced Support Vector Machines. IEEE Trans.Neural Network. 14(6), 1449–1459 (2003)
Mangasarian, O.L.: Generalized Support Vector Machines. In: Smola, A., Bartlett, P., Scholkopf, B., Schurrmans, D. (eds.) Advances in large Margin Classifiers, pp. 35–146. MIT Press, Cambridge (2000); ISBN: 0-262-19448-1
Newman, D.J., Hettich, S., Blake, C.L.S., Merz, C.J.: UCI repository of machine learning database. Dept. of Information and Computer Science. University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/~MLRepository.html
Ralambondrainy, H.: A conceptual version of the K-Means algorithm. Pattern Recognition Letters 16, 1147–1157 (1995)
San, O.M., Huynh, V.N., Nakamori, Y.: An alternative extension of the k-means algorithm for clustering categorical data. International Journal Applied Mathematic Computing Science 14(2), 241–247 (2004)
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Purnami, S.W., Zain, J.M., Embong, A. (2011). Reduced Support Vector Machine Based on k-Mode Clustering for Classification Large Categorical Dataset. In: Zain, J.M., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22191-0_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-22191-0_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22190-3
Online ISBN: 978-3-642-22191-0
eBook Packages: Computer ScienceComputer Science (R0)