Abstract
Since rough sets were introduced by Pawlak about 25 years ago they have become a central part of soft computing. Recently Lingras presented a rough k-means clustering algorithm which assigns the data objects to lower and upper approximations of clusters. In our paper we introduce a rough k-medoids clustering algorithm and apply it to four different data sets (synthetic, colon cancer, forest and control chart data). We compare the results of these experiments to Lingras rough k-means and discuss the strengths and weaknesses of the rough k-medoids.
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
Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man, and Cybernetics 28, 301–315 (1998)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227 (1979)
do Prado, H.A.F., Engel, P.M., Filho, H.C.: Rough clustering: An alternative to find meaningful clusters by using the reducts from a dataset. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS, vol. 2475, pp. 234–238. Springer, Berlin (2002)
Kaufman, J., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, New York (1990)
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)
Mitra, S.: An evolutionary rough partitive clustering. Pattern Recognition Letters 25, 1439–1449 (2004)
Pawlak, Z.: Rough sets. International Journal of Information and Computer Sciences 11, 145–172 (1982)
Peters, G.: Outliers in rough k-means clustering. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 702–707. Springer Verlag, Heidelberg (2005)
Peters, G.: Some refinements of rough k-means. Pattern Recognition 39, 1481–1491 (2006)
Voges, K.E., Pope, N.K., Brown, M.R.: Cluster Analysis of Marketing Data Examining On-Line Shopping Orientation: A Comparision of k-Means and Rough Clustering Approaches. In: Heuristics and Optimization for Knowledge Discovery, pp. 207–224. Idea Group Publishing, Hershey (2002)
Yao, Y.Y., Li, X., Lin, T.Y., Liu, Q.: Representation and classification of rough set models. In: Proceedings Third International Workshop on Rough Sets and Soft Computing, San Jose, CA, pp. 630–637 (1994)
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
Peters, G., Lampart, M. (2006). A Partitive Rough Clustering Algorithm. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_68
Download citation
DOI: https://doi.org/10.1007/11908029_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47693-1
Online ISBN: 978-3-540-49842-1
eBook Packages: Computer ScienceComputer Science (R0)