Abstract
We propose a natural neighbor inspired O(\(n \sqrt{n}\)) hybrid clustering algorithm that combines medoid-based partitioning and agglomerative hierarchial clustering. This algorithm works efficiently by inheriting partitioning clustering strategy and operates effectively by following hierarchial clustering. More importantly, the algorithm is designed by taking into account the specific features of sequential data modeled in metric space. Experimental results demonstrate the virtue of our approach.
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
Rousseeuw, P.J., Leroy, A.M.: Robust regression and outlier detection. John Wiley, New York (1987)
Teitz, M.B., Bart, P.: Heuristic methods for estimating the generalized vertex median of a weighted graph. Operations Research 16, 955–961 (1968)
Estivill-Castro, V., Yang, J.: Clustering web visitors by fast, robust and convergent algorithms. Int. J. of Fundations of Computer Science 13(4), 497–520 (2002)
Murtagh, F.: Comments on parallel algorithms for hierarchical clustering and cluster validity. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(10), 1056–1057 (1992)
Perkowitz, M., Etzioni, O.: Adaptive Web sites: Automatically synthesizing Web pages. In: Proc. of the 15th National Conf. on AI, Madison, WI, American Association for Artificial Intelligence, pp. 727–732. AAAI Press, Menlo Park (1998)
Shahabi, C., Zarkesh, A.M., Adibi, J., Shah, V.: Knowledge discovery from users Web page navigation. In: Proc. of the IEEE RIDE 1997 (1997)
Morzy, T., Wojciechowski, M., Zakrzewicz, M.: Scalable hierarchical clustering method for sequences of categorical values. In: Proc. of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Kowloon, Hong Kong, pp. 282–293 (2001)
Guralnik, V., Karypis, G.: A scalable algorithm for clustering sequential data. In: Proc. of the 1st IEEE Int. Conf. on Data Mining, San Jose, California, USA, pp. 179–186 (2001)
Kato, H., Nakayama, T., Yamane, Y.: Navigation analysis tool based on the correlation between contents distribution and access patterns. In: Proc. of the Web Mining for E-Commerce Workshop, Boston, MA, USA (2000)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, San Diego (1998)
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
Yang, J., Lee, I. (2006). Hybrid O(\(n \sqrt{n}\)) Clustering for Sequential Web Usage Mining. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_115
Download citation
DOI: https://doi.org/10.1007/11941439_115
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
eBook Packages: Computer ScienceComputer Science (R0)