S. Guha, R. Rastogi and K. Shim. CURE: An efficient algorithm for clustering large databases, Proceedings of ACM-SIGMOD 1998. Home/Search Document Not in Database Summary Related Articles Check |
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....context of data mining the data set being too large to fit in main memory, it is more relevant to investigate clustering algorithms meeting the specific requirement of minimizing the I O operations. Some of the major clustering algorithms proposed in the context of data mining are BIRCH[14] CURE[10], PAM[12] CLARANS[12] DBSCAN[4] BUBBLE[7] MAFIA [8] ITERATE , CHAMELON[11] etc. It is to be noted that the basic principle of clustering hinges on a concept of distance metric or similarity metric. Thus the clustering techniques that are designed mostly for numeric data, exploit the inherent ....
. S. Guha, R. Rastogi and K. Shim. CURE: An efficient algorithm for clustering large databases. Proceedings of ACM-SIGMOD 1998.
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S. Guha, R. Rastogi and K. Shim. CURE: An efficient algorithm for clustering large databases, Proceedings of ACM-SIGMOD 1998.
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