Abstract
Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of data by means of a method that summarizes the database. This is done using a structure called CM-tree. In order to test our method, the K-Modes and Click clustering algorithms were used with several databases. Experiments demonstrate that the proposed summarization method improves execution time, without losing clustering quality.
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
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, New York (1990)
Jain, A.K., Dubes, R.C.: Algorithm for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
Andritsos, P., Tsaparas, P., Miller, R.J., Sevcik, K.C.: LIMBO: A scalable Algorithm to Cluster Categorical Data. Technical report, University of Toronto, Department of Computer Science, CSRG-467 (2004)
Huang, Z.: A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. In: Sigmod Workshop on Research Issues on Data Mining and Knowledge Discovery, pp. 1–8 (1997)
Ganti, V., Gehrkeand, J., Ramakrishanan, R.: CACTUS-Clustering Categorical Data Using Summaries. In: Proceeding of the 5th ACM Sigmod International Conference on Knowledge Discovery in Databases, San Diego, California, pp. 73–83 (1999)
Guha, S., Rastogi, R., Shim, K.: Rock: A robust clustering algorithm for categorical attributes. In: Proceeding of the 15th International Conference on Data Engineering (ICDE), Sydney, pp. 512–521 (1999)
Barbará, D., Li, Y., Couto, J.: Coolcat: an entropy-based algorithm for categorical clustering, pp. 582–589. ACM Press, New York (2002)
Zaki, M.J., Peters, M., Assent, I., Seidl, T.: CLICK: An Effective algorithm for Mining Subspace Clustering in categorical datasets. In: Proceeding of the eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 733–742 (2005)
Gowda, K., Diday, E.: Symbolic Clustering Using a New Dissimilarity Measure. Pattern Recognition 24(6), 567–578 (1991)
Rendón, E., Sánchez, J.S.: Clustering Based on Compressed Data for Categorical and Mixed Attibutes. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 817–825. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rendón, E., Sánchez, J.S., Garcia, R.A., Abundez, I., Gutierrez, C., Gasca, E. (2008). Data Reduction Method for Categorical Data Clustering. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88309-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-88309-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88308-1
Online ISBN: 978-3-540-88309-8
eBook Packages: Computer ScienceComputer Science (R0)