Abstract
In this paper we developed a new methodology for grouping objects described by nominal attributes. We introduced a definition of condition’s domination within each pair of cluster, and next the measure of ω-distinguishability of clusters for creating a junction of clusters. The developed method is hierarchical and agglomerative one and can be characterized both by high speed of computation as well as extremely good accuracy of clustering.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Apostolico, R., Bock, M.E., Lonardi, S.: Monotony of surprise in large-scale quest for unusual words. In: Proceedings of the 6th International Conference on Research in Computational Molecular Biology, Washington, DC, April 18-21, pp. 22–31 (2002)
Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3, 32–57 (1973)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1-3-8 (1977)
Gionis, A., Mannila, H.: Finding recurrent sources in sequences. In: Proceedings of the 7th International Conference on Research in Principles of Database Systems, Tucson, AZ, May 12-14, pp. 249–256 (2003)
Johnson, S.C.: Hierarchical Clustering Schemes. Psychometrika 2, 241–254 (1967)
Krawczak, M., Szkatuła, G.: On time series envelopes for classification problem. In: Developments of Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets, vol. II, pp. 149–164 (2010)
Krawczak, M., Szkatuła, G.: Time series envelopes for classification. In: Proceedings of the Conference: 2010 IEEE International Conference on Intelligent Systems, London, UK, July 7-9, pp. 156–161 (2010)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a Novel Symbolic Representation of Time Series. Data Min. Knowledge Disc. 2(15), 107–144 (2007)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Wang, B.: A New Clustering Algorithm on Nominal Data Sets. In: Proceedings of International MultiConference on Engineers and Computer Scientists, IMECS 2010, Hong Kong, March 17-19 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krawczak, M., Szkatuła, G. (2012). A Clustering Algorithm Based on Distinguishability for Nominal Attributes. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-29350-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29349-8
Online ISBN: 978-3-642-29350-4
eBook Packages: Computer ScienceComputer Science (R0)