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Clustering Association Rules

Lent, B. and Swami, A. and Widom, J. (1996) Clustering Association Rules. Technical Report. Stanford InfoLab. (Publication Note: Thirteenth International Conference on Data Engineering, April 7-11, 1997 Birmingham U.K (ICDE '97))




We consider the problem of clustering two-dimensional association rules in large databases. We present a geometric-based 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

Item Type:Techreport (Technical Report)
Uncontrolled Keywords:data mining, association rules, clustering, segmentation, database
Subjects:Computer Science > Data Mining
Related URLs:Project Homepage
ID Code:158
Deposited By:Import Account
Deposited On:25 Feb 2000 16:00
Last Modified:09 Dec 2008 09:01

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