Abstract
This paper presents a visual method of cluster validation using the Fastmap algorithm. Two problems are tackled with Fastmap in the interactive process of discovering interesting clusters from real world databases. That is, (1) to verify separations of clusters created by a clustering algorithm and (2) to determine the number of clusters to be produced. They are achieved through projecting objects and clusters by Fastmap to the 2D space and visually examining the results by humans. We use a real example to show how this method has been used in discovering interesting clusters from a real data set.
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References
Agrawal, R., Gehrke, J, Gunopulos, D. and Raghavan, P. (1998) Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of SIGMOD Conference.
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 Proceedings of the 2nd International Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, USA.
Faloutsos, C. and Lin, K., (1995) Fastmap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In Proceedings of ACM-SIGMOD, pp. 163–174.
Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition. Academic Press.
Ganti, V., Ramakrishnan, R., Gehrke, J, Powell, A. L. and French, J. C. (1999) Clustering large datasets in arbitrary metric spaces. ICDE 1999, pp. 502–511.
Huang, Z. (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, Vol. 2, No. 3, pp. 283–304.
Jain, A. K. and Dubes, R. C. (1988) Algorithms for Clustering Data. Prentice Hall.
Ng, R. and Han, J. (1994) Efficient and effective clustering methods for spatial data mining. In Proceedings of VLDB, 1994.
Theodoridis, S. and Koutroumbas, K. (1999) Pattern Recognition. Academic Press.
Young, F. W. (1987) Multidimensional Scaling: History, Theory and Applications. Lawrence Erlbaum Associates.
Zhang, T. and Ramakrishnan, R. (1997) BIRCH: A new data clustering algorithm and its applications. Data Mining and Knowledge Discovery, Vol. 1, No. 2, pp. 141–182.
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Huang, Z., Lin, T. (2000). A Visual Method of Cluster Validation with Fastmap. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_18
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DOI: https://doi.org/10.1007/3-540-45571-X_18
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