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CBCM: A cell-based clustering method for data mining applications. (English) Zbl 1020.68580
Meng, Xiaofeng (ed.) et al., Advance in web-age information management. Third international conference, WAIM 2002, Beijing, China, August 11-13, 2002. Proceedings. Berlin: Springer. Lect. Notes Comput. Sci. 2419, 291-302 (2002).
Summary: 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.
For the entire collection see [Zbl 1010.68856].
MSC:
68U99 Computing methodologies and applications
68P15 Database theory
68U35 Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.)
68P20 Information storage and retrieval of data
Software:
CBCM
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