Abstract
In this paper we present a novel clustering method that can deal with both numerical and categorical data with a novel clustering objective and without the need of a user specified parameter. Our approach is based on an extension of database relation – hyperrelations. A hyperrelation is a set of hypertuples, which are vectors of sets.
In this paper we show that hyperrelations can be exploited to develop a new method for clustering both numerical and categorical data. This method merges hypertuples pairwise in the direction of increasing the density of hypertuples. This process is fully automatic in the sense that no parameter is needed from users. Initial experiments with artificial and real-world data showed this novel approach is promising.
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References
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
Gibson, D., Kleinberg, J., Raghavan, P.: Clustering categorical data: An approach based on dynamical systems. In: Proc. 24th International Conference on Very Large Databases, New York (1998)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Menlo Park (1996)
Wang, W., Yang, J., Muntz, R.: STING: A statistical information grid approach to spatial data mining. In: Proc. 23rd Int. Conf. on Very Large Databases, pp. 186–195. Morgan Kaufmann, San Francisco (1997)
Wang, H., Düntsch, I., Bell, D.: Data reduction based on hyper relations. In: Proceedings of KDD 1998, New York, pp. 349–353 (1998)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Chichester (1990)
Schikuta, E.: Grid clustering: an efficient hierarchical clustering method for very large data sets. In: Proc. 13th Int. Conf. on Pattern Recognition, vol. 2, pp. 101–105. IEEE Computer Society Press, Los Alamitos (1996)
Ester, M., Kriegel, H.P., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: Proc. 24th International Conference on Very Large Databases (1998)
Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. John Wiley & Sons, Chichester (1973)
Guha, S., Rastogi, R., Shim, K.: ROCK: A robust clustering algorithm for categorical attributes. Technical Report 208, Bell Laboratories (1998)
Han, E.H., Karypis, G., Kumar, V., Mobasher, B.: Clustering based on association rule hypergraphs. In: 1997 SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (1997)
Gray, B., Orlowska, M.E.: Clustering categorical attributes into interesting association rules. In: Proc. PAKDD 1998 (1998)
Hilderman, R.J., Carter, C.L., Hamilton, H.J., Cercone, N.: Mining market basket data using share measures and characterized itemsets. In: Proc. PAKDD 1998 (1998)
Bell, D.A., McErlean, F., Stewart, P., Arbuckle, W.: Clustering related tuples in databases. Computer Journal 31(3), 253–257 (1988)
Stewart, P., Bell, D.A., McErlean, F.: Some aspects of a physical database design and reorganisation tool. Journal of Data and Knowledge Engineering, 303–322 (1989)
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Wang, H. (2006). A Novel Clustering Method Based on Spatial Operations. In: Bell, D.A., Hong, J. (eds) Flexible and Efficient Information Handling. BNCOD 2006. Lecture Notes in Computer Science, vol 4042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788911_12
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DOI: https://doi.org/10.1007/11788911_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35969-2
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