Clustering Association Rules
نویسندگان
چکیده
We consider the problem of clustering two-dimensional association rules in large databases. We present a geometricbased algorithm, BitOp, for performing the clustering, embedded within an association rule clustering system, ARCS. Association rule clustering is useful when the user desires to segment the data. We measure the quality of the segmentation generated by ARCS using the Minimum Description Length (MDL) principle of encoding the clusters on several databases including noise and errors. Scale-up experiments show that ARCS, using the BitOp algorithm, scales linearly with the amount of data.
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تاریخ انتشار 1997