Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
G-TREACLE: a new grid-based and tree-alike pattern clustering technique for large databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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This investigation presents a method named FARM that combines a grid-based algorithm with the density-based approach for clustering data in data mining applications. In the FARM clustering method, the number of separate clusters need not be specified but only the number of divisions of the clusters is required. Experimental results indicate that the proposed method clusters correctly. It filters 98.8% of the noise, and the data set accuracy exceeds 99.7%. The most surprising result is the time required to process data sets. Processing 575,000 data sets takes only 0.33 second - much less time than any currently known clustering algorithm.