Abstract
This paper compares the results of clustering obtained using a modified K-means algorithm with the conventional clustering process. The modifications to the K-means algorithm are based on the properties of rough sets. The resulting clusters are represented as interval sets. The paper describes results of experiments used to create conventional and interval set representations of clusters of web users on three educational web sites. The experiments use secondary data consisting of access logs from the World Wide Web. This type of analysis is called web usage mining, which involves applications of data mining techniques to discover usage patterns from the web data. Analysis shows the advantages of the interval set representation of clusters over conventional crisp clusters.
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
A. Joshi and R. Krishnapuram, Robust Fuzzy Clustering Methods to Support Web Mining, in the proceedings of the workshop on Data Mining and Knowledge Discovery, SIGMOD’ 98, 1998, 15/1–15/8.
R. Hathaway and J. Bezdek, Switching Regression Models and Fuzzy Clustering, IEEE Transactions of Fuzzy Systems, vol. 1, no. 3, 1993, 195–204.
R. Krishnapuram, H. Frigui, and O. Nasraoui, Fuzzy and Possibilistic Shell Clustering Algorithms and their Application to Boundary Detection and Surface Approximation, Parts I and II, IEEE Transactions on Fuzzy Systems, vol. 3, no. 1, 1995, 29–60.
R. Krishnapuram and J. Keller, A Possibilistic Approach to Clustering, IEEE Transactions, vol. 1, no. 2, 1993, 98–110.
P. Lingras, Unsupervised Rough Set Classification using GAs, Journal of Intelligent Information Systems, vol. 16, no. 3, 2001, 215–228.
P. Lingras, Rough Set Clustering For Web Mining, in the proceedings of 2002 IEEE International Conference on Fuzzy Systems, 2002.
P. Lingras, and C. West, Interval Set Clustering of Web Users with Rough K-means, submitted to Journal of Intelligent Information Systems, 2002.
Z. Pawlak, Rough Sets, International Journal of Information and Computer Sciences, vol. 11, 1982, 145–172.
A. Skowron and J. Stepaniuk, Information Granules in Distributed Environment, in New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, Setsuo Ohsuga, Ning Zhong, Andrzej Skowron, Ed., Springer-Verlag, Lecture Notes in Artificial Intelligence 1711, Tokyo, 1999, 357–365.
Y. Y. Yao, X. Li, T. Y. Lin and Q. Liu, Representation and Classification of Rough Set Models, in the proceedings of Third International Workshop on Rough Sets and Soft Computing, 1994, 630–637.
P. Lingras, M. Hogo and M. Snorek, Interval Set Clustering of Web Users using Modified Kohonen Self-Organization Maps based on the Properties of Rough Sets, submitted to Web Intelligent and Agent Systems: an International Journal, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lingras, P., Yan, R., West, C. (2003). Comparison of Conventional and Rough K-Means Clustering. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_17
Download citation
DOI: https://doi.org/10.1007/3-540-39205-X_17
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-14040-5
Online ISBN: 978-3-540-39205-7
eBook Packages: Springer Book Archive