Abstract
An Evidence-Based Spectral Clustering (EBSC) algorithm that works well for data with mixed numeric and nominal features is presented. A similarity measure based on evidence accumulation is adopted to define the similarity measure between pairs of objects, which makes no assumptions of the underlying distributions of the feature values. A spectral clustering algorithm is employed on the similarity matrix to extract a partition of the data. The performance of EBSC has been studied on real data sets. Results demonstrate the effectiveness of this algorithm in clustering mixed data tasks. Comparisons with other related clustering schemes illustrate the superior performance of this approach.
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
Huang, Z.: Clustering Large Data Sets with Mixed Numeric and Categorical Values. In: Proceedings of the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Singapore, pp. 21–34 (1997)
Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery 2, 283–304 (1998)
Li, C., Biswas, G.: Unsupervised Learning with Mixed Numeric and Nominal Data. IEEE Trans. Knowl. Data Eng. 14, 673–690 (2002)
Fred, A.L.N., Jain, A.K.: Data Clustering using Evidence Accumulation. In: Proc. of the 16th Intl. Conference on Pattern Recognition ICPR 2002, Quebec City, pp. 276–280 (2002)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On Spectral Clustering: Analysis and an algorithm. NIPS, pp. 849–856 (2001)
McKusick, K.B., Thompson, K.: COBWEB/3: A portable implementation Moffett Field. NASA Ames Research Center, CA (1990)
Reich, Y., Fenves, S.: The formation and use of abstract concepts in design. In: Fisher, D., Pazzani, M., Langley, P. (eds.) Concept Formation: Knowledge and Experience in Unsupervised Learning, pp. 323–353. Morgan Kaufmann, Los Altos (1991)
Cheeseman, P., Stutz, J.: Bayesian classification (AutoClass): Theory and results. In: Fayyad, U.M., Diatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 153–180. AAAI Press/The MIT Press (1996)
Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine Learning 2, 139–172 (1987)
Topchy, A., Jain, A.K., Punch, W.: A Mixture Model of Clustering Ensembles. In: Proceedings of the SIAM International Conference on Data Mining, Lake Buena Vista, Florida, pp. 22–24 (2004)
Jain, A.K., Fred, A.: Evidence accumulation clustering based on the k-means algorithm. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 442–451. Springer, Heidelberg (2002)
He, Z., Xu, X., Deng, S.: A cluster ensemble method for clustering categorical data. Information Fusion 6, 143–151 (2005)
Gluck, M., Corter, J.: Information, Uncertainty, and the Utility of Categories. In: Proc. Seventh Ann. Conf. Cognitive Soc., pp. 283–287 (1985)
Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistics Quaterly 2, 83–97 (1955)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luo, H., Kong, F., Li, Y. (2006). Clustering Mixed Data Based on Evidence Accumulation. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_38
Download citation
DOI: https://doi.org/10.1007/11811305_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)