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))
| BibTeX | DublinCore | EndNote | HTML |
![[img]](/style/images/fileicons/application_pdf.png)
| PDF 1130Kb |
Abstract
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 | |
| Projects: | MIDAS | |
| Related URLs: | Project Homepage | http://infolab.stanford.edu/midas/midas.html |
| ID Code: | 158 | |
| Deposited By: | Import Account | |
| Deposited On: | 25 Feb 2000 16:00 | |
| Last Modified: | 09 Dec 2008 09:01 |
Download statistics
Repository Staff Only: item control page
