Abstract
Clustering is one of the most important topics in the field of knowledge discovery from databases. Specifically, hierarchical clustering is useful because it can be used to interactively guide users in browsing a huge database. In many cases, database clustering can be modeled as a graph partitioning problem, because a database with a distance function defined on it can be regarded as an edge weighted graph. So process of MST(Minimal Spanning Tree) construction is a possible solution to this problem.
In this paper, we propose an efficient MST construction method for a database with an arbitrary distance function on it. Our method utilizes a metric index to reduce the number of distance calculations needed to construct an MST. For this purpose, we introduce a new metric index named metric matrix. Experimental results show that our method can reduce the number of distance calculations needed in comparison with the classical method.
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Ester, M. Kriegel, H.-P. Sander, J. and Xu, X. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise, In Proc. of the 2nd Int’l Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon.
Tolga B. and Ozsoyoglu, M. (1997) Distance-based indexing for highdimensional metric spaces, Proceedings of the ACM SIGMOD Conference on Management of Data, Tucson, Arizona, May 1997
Ishikawa, M. Notoya, J. Chen, H. and Ohbo, N. (1999) A metric index MI-tree, IPSJ Transations on Databases, Vol.40, No.SIG6, 1999.
Tarjan, R. E. (1983) Data Structure and Network Algorithm, Society for Industrial and Applied Mathematics.
Brin, S. (1995) Near neighbour search in large metric spaces, Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995.
Ciaccia, P. Patella, M. and ZezulaBib, P. (1997) M-tree: An efficient access method for similarity search in metric spaces, Proceedings of the 23rd VLDB Conference, Athenes, Greece, 1997.
Jain, A. K. and DubesBib R. C. (1988) Algorithm for Clustering Data, Printice Hall.
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© 2000 Springer Science+Business Media New York
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Ishikawa, M., Liu, Y., Furuse, K., Chen, H., Ohbo, N. (2000). MST Construction with Metric Matrix for Clustering. In: Arisawa, H., Catarci, T. (eds) Advances in Visual Information Management. VDB 2000. IFIP — The International Federation for Information Processing, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35504-7_21
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DOI: https://doi.org/10.1007/978-0-387-35504-7_21
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-4457-6
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