Abstract
This paper presents a novel hybrid clustering approach that takes advantage of the efficiency of k -Means clustering and the effectiveness of hierarchical clustering. It employs the combination of geometrical information defined by k -Means and topological information formed by the Voronoi diagram to advantage. Our proposed approach is able to identify clusters of arbitrary shapes and clusters of different densities in O(n) time. Experimental results confirm the effectiveness and efficiency of our approach.
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
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Maths and Statistics Problems, vol. 1, pp. 281–297 (1967)
Murtagh, F.: Comments on Parallel Algorithms for Hierarchical Clustering and Cluster Validity. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 1056–1057 (1992)
Lin, C.R., Chen, M.S.: Combining Partitional and Hierarchical Algorithms for Robust and Efficient Data Clustering with Cohesion Self-Merging. IEEE Transactions on Knowledge and Data Engineering 17, 145–159 (2005)
Guha, S., Rastogi, R., Shim, K.: CURE: An Efficient Clustering Algorithm for Large Databases. In: Haas, L.M., Tiwary, A. (eds.) Proceedings of the ACM SIGMOD 1998 International Conference on Management of Data, Seattle, Washington, pp. 73–84. ACM Press, New York (1998)
Murty, N.M., Krishna, G.: A Hybrid Clustering Procedure for Concentric and Chain-like Clusters. International Journal of Computer and Information Sciences 10, 397–412 (1981)
Boros, E., Hammer, P.L.: On Clustering Problems with Connected Optima in Euclidean Spaces. Discrete Mathematics 75, 81–89 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, I., Yang, J. (2005). Hybrid Agglomerative Clustering for Large Databases: An Efficient Interactivity Approach. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_115
Download citation
DOI: https://doi.org/10.1007/11589990_115
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
eBook Packages: Computer ScienceComputer Science (R0)