Abstract
Many projects in data mining face, besides others, the following two challenges. On the one hand concepts to deal with uncertainty - like probability, fuzzy set or rough set theory - play a major role in the description of real life problems. On the other hand many real life situations are characterized by constant change - the structure of the data changes. For example, the characteristics of the customers of a retailer may change due to changing economical parameters (increasing oil prices etc.). Obviously the retailer has to adapt his customer classification regularly to the new situations to remain competitive. To deal with these changes dynamic data mining has become increasingly important in several practical applications. In our paper we utilize rough set theory to deal with uncertainty and suggest an engineering like approach to dynamic clustering that is based on rough k-means.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lingras, P., West, C.: Interval set clustering of web users with rough k-means. Technical Report 2002-002, Department of Mathematics and Computer Science, St. Mary’s University, Halifax, Canada (2002)
Lingras, P., West, C.: Interval set clustering of web users with rough k-means. Journal of Intelligent Information Systems 23, 5–16 (2004)
Lingras, P., Hogo, M., Snorek, M.: Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets. Web Intelligence and Agent Systems 2(3), 217–225 (2004)
Lingras, P.: Applications of rough set based k-means, Kohonen SOM, GA clustering. In: Peters, J., Skowron, A., Marek, V., Orlowska, E., Slowinski, R., Ziarko, W. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 120–139. Springer, Heidelberg (2007)
Mitra, S.: An evolutionary rough partitive clustering. Pattern Recognition Letters 25, 1439–1449 (2004)
Peters, G.: Some refinements of rough k-means. Pattern Recognition 39, 1481–1491 (2006)
Peters, G., Lampart, M., Weber, R.: Evolutionary rough k-medoids clustering. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets VIII. LNCS, vol. 5084, pp. 289–306. Springer, Heidelberg (2008)
Crespo, F., Weber, R.: A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets and Systems 150(2), 267–284 (2005)
Bezdek, J.: Pattern Recognition with Fuzzy Objective Algorithms. Plenum Press, New York (1981)
Weber, R.: Fuzzy clustering in dynamic data mining - techniques and applications. In: Valente de Oliveira, J., Pedrycz, W. (eds.) Advances in Fuzzy Clustering and Its Applications, pp. 315–332. John Wiley and Sons, Hoboken (2007)
Bezdek, J., Pal, N.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man, and Cybernetics 28, 301–315 (1998)
Davies, D., Bouldin, D.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227 (1979)
Windham, M.: Cluster validity for fuzzy clustering algorithms. Fuzzy Sets and Systems 5, 177–185 (1981)
Lingras, P., Yan, R., West, C.: Comparison of conventional and rough k-means clustering. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 130–137. Springer, Heidelberg (2003)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Boston (1989)
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
Peters, G., Weber, R. (2008). A Dynamic Approach to Rough Clustering. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-88425-5_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88423-1
Online ISBN: 978-3-540-88425-5
eBook Packages: Computer ScienceComputer Science (R0)