Abstract
Data mining applications have recently required a large amount of high-dimensional data. However, most clustering methods for the data miming applications do not work efficiently for dealing with large, high-dimensional data because of the so-called ‘curse of dimensionality’ and the limitation of available memory. In this paper, we propose a new cell-based clustering method (CBCM) which is more efficient for large, high-dimensional data than the existing clustering methods. Our CBCM provides an efficient cell creation algorithm using a space-partitioning technique and uses a filtering-based index structure using an approximation technique. In addition, we compare the performance of our CBCM with the CLIQUE method in terms of cluster construction time, precision, and retrieval time.
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© 2002 Springer-Verlag Berlin Heidelberg
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Chang, JW. (2002). CBCM: A Cell-Based Clustering Method for Data Mining Applications. In: Meng, X., Su, J., Wang, Y. (eds) Advances in Web-Age Information Management. WAIM 2002. Lecture Notes in Computer Science, vol 2419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45703-8_27
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DOI: https://doi.org/10.1007/3-540-45703-8_27
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