Alexander Strehl, Gunjan K. Gupta, Joydeep Ghosh  Home/Search
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Abstract: Association rule mining is one of the most important procedures in data mining. In industry applications, often more than 10,000 rules are discovered. To allow manual insepection and support knowledge discovery the number of rules has to be reduced signi cantly by techniques such as pruning or grouping. In this paper, we present a new normalized distance metric to group association rules. Based on these distances, an agglomerative clustering algoritm is used to cluster the rules. Also the... (Update)
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BibTeX entry: (Update)
@misc{ strehl-distance,
author = "Alexander Strehl and Gunjan K. Gupta and Joydeep Ghosh",
title = "Distance Based Clustering of Association Rules",
url = "citeseer.ist.psu.edu/732874.html" }
Citations (may not include all citations):921 Mining association rules between sets of items in large data.. - Agrawal, Imielinski et al. - 1993
213 Discovery of multiple-level association rules from large dat.. - Han, Fu - 1995
208 Fast algorithm for mining association rules in large databas.. (context) - Agrawal, Srikant - 1994
201 Cluster Analysis (context) - Everitt - 1980
131 Data mining: An overview from a database perspective - Chen, Han et al. - 1996
36 Pruning and grouping discovered association rules - Toivonen, Klemettinen et al. - 1995
22 Using a hashbased method with transaction trimming for minin.. - Park, Chen et al. - 1997
7 Applied Multivariate Statistical Analysis (context) - Johnson, Wichern - 1982
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