Abstract
Clustering is one of the most important tasks in data mining. The K-means algorithm is the most popular one for achieving this task because of its efficiency. However, it works only on numeric values although data sets in data mining often contain categorical values. Responding to this fact, the K-modes algorithm is presented to extend the K-means algorithm to categorical domains. Unfortunately, it suffers from computing the dissimilarity between each pair of objects and the mode of each cluster. Aiming at addressing these problems confronting K-modes, we present a new algorithm called K-distributions in this paper. We experimentally tested K-distributions using the well known 36 UCI data sets selected by Weka, and compared it to K-modes. The experimental results show that K-distributions significantly outperforms K-modes in term of clustering accuracy and log likelihood.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys (CSUR) 31, 264–323 (1999)
MacQueen, J.B.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. Univ.of California, Berkeley, USA: Statistics and Probability, pp. 281–297 (1967)
Huang, Z.: A fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. In: Proceedings of the SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery. Tucson, Arizona, USA, pp. 146–151 (1997)
Merz, C., Murphy, P., Aha, D.: UCI Repository of Machine Learning Databases. In: Dept of ICS, University of California, Irvine (1997), http://www.ics.uci.edu/mlearn/MLRepository.html
Witten, I.H., Frank, E.: Data Mining: Practical Machine Mearning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005), http://prdownloads.sourceforge.net/weka/datasets-UCI.jar
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers. Machine Learning 29, 131–163 (1997)
Grossman, D., Domingos, P.: Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood. In: Proceedings of the Twenty-First International Conference on Machine Learning, Banff, Canada, pp. 361–368. ACM Press, New York (2004)
Guo, Y., Greiner, R.: Discriminative Model Selection for Belief Net Structures. In: Proceedings of the Twentieth National Conference on Artificial Intelligence, pp. 770–776. AAAI Press, California, USA (2005)
Nadeau, C., Bengio, Y.: Inference for the Generalization Error. In: Advances in Neural Information Processing Systems, vol. 12, pp. 307–313. MIT Press, Cambridge (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cai, Z., Wang, D., Jiang, L. (2007). K-Distributions: A New Algorithm for Clustering Categorical Data. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-74205-0_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
Online ISBN: 978-3-540-74205-0
eBook Packages: Computer ScienceComputer Science (R0)