Abstract
Clustering categorical data is an important and challenging data analysis task. In this paper, we explore the use of kernel K-means to cluster categorical data. We propose a new kernel function based on Hamming distance to embed categorical data in a constructed feature space where the clustering is conducted. We experimentally evaluated the quality of the solutions produced by kernel K-means on real datasets. Results indicated the feasibility of kernel K-means using our proposed kernel function to discover clusters embedded in categorical data.
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References
Andritsos, P., Tsaparas, P., Miller, R.J., Sevcik., K.C.: LIMBO: Scalable Clustering of Categorical Data. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992. Springer, Heidelberg (2004)
Barbara, D., Couto, J., Li, Y.: Coolcat: An Entropy-based algorithm for Categorical Clustering. In: Proceedings of the 11th ACM Conference on Information and Knowledge Management (CIKM 2002), McLean, Virginia, USA, November 2002, pp. 582–589. ACM Press, New York (2002)
Ben-hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. Journal of Machine Learning Research 2, 125–137
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California, Department of Information and Computer Science, Irvine, CA, http://www.ics.uci.edu/~mlearn/MLRepository.html.
Ganti, V., Gehrke, J., Ramakrishnan, R.: CACTUS: Clustering Categorical Data using Summaries. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA, August 1999, pp. 73–83. ACM Press, New York (1999)
Girolami, M.: Mercer Kernel Based Clustering in Feature Space. IEEE Transactions on Neural Networks 13(4), 780–784 (2002)
Gibson, D., Kleinberg, J., Raghavan, P.: Clustering Categorical Data: An Approach Based on Dynamical Systems. In: Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), New York, USA, August 1998, pp. 311–322. Morgan Kaufmann, San Francisco (1998)
Gluck, A., Corter, J.: Information, Uncertainty, and the Utility of Categories. In: Proceedings of the 7th Annual Conference of the Cognitive Science Society, Irvine, California, pp. 283–287. Laurence Erlbaum Associates, Mahwah (1985)
Guha, S., Rastogi, R., Shim, K.: ROCK: A Robust Clustering Algorithm for Categorical Attributes. Journal of Information Systems 25(5), 345–366 (2000)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17(2–3), 107–145 (2001)
Huang, Z.: Extensions to the K-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery 2(3), 283–304 (1998)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)
Katsavounidis, I., Kuo, C., Zhang, Z.: A New Initialization Technique for Generalized Lloyd Iteration. IEEE Signal Processing Letters 1(10), 144–146 (1994)
Kondor, R.I., Lafferty, J.: Diffusion Kernels on Graphs and Other Discrete Structures. In: Sammut, C., Hoffmann, A.G. (eds.) Proceedings of the 19th International Conference on Machine Learning (ICML 2002), pp. 315–322. Morgan Kaufmann, San Francisco (2002)
Larsen, B., Aone, C.: Fast and Effective Text Mining Using Linear-time Document Clustering. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, August 1999, pp. 16–22. ACM Press, New York (1999)
Lodhi, H., Shawe-Taylor, J., Cristiani, N., Watkins, C.: Text Classification using String Kernels. Journal of Machine Learning Research 2, 419–444
Shawe-Taylor, J., Cristiani, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2003)
Slonim, N., Tibshy, N.: Agglomerative Information Bottleneck. In: Proceedings of the Neural Information Processing Systems Conference 1999 (NIPS 1999), Beckenridge (1999)
Steinbach, M., Karypis, G., Kumar, V.: A Comparison of Document Clustering Technique. Technical Report #00–034, University of Minnesota, Department of Computer Science and Egineering
Zaki, M.J., Peters, M.: CLICK: Mining Subspaces Clusters in Categorical Data via K-partite Maximal Cliques. TR 04-11, CS Dept., RPI (2004)
Zhang, R., Rudnicky, A.: A Large Scale Clustering Scheme for Kernel K-means. In: Proceedings of the 16th International Conference on Pattern Recognition (ICPR 2002), Quebec City, Canada, August 2002, pp. 289–292 (2002)
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Couto, J. (2005). Kernel K-Means for Categorical Data. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_5
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DOI: https://doi.org/10.1007/11552253_5
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