The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - January/February (2001 vol.13)
pp: 64-78
ABSTRACT
<p><b>Abstract</b>—Association-rule mining has heretofore relied on the condition of high support to do its work efficiently. In particular, the well-known a priori algorithm is only effective when the only rules of interest are relationships that occur very frequently. However, there are a number of applications, such as data mining, identification of similar web documents, clustering, and collaborative filtering, where the rules of interest have comparatively few instances in the data. In these cases, we must look for highly correlated items, or possibly even causal relationships between infrequent items. We develop a family of algorithms for solving this problem, employing a combination of random sampling and hashing techniques. We provide analysis of the algorithms developed and conduct experiments on real and synthetic data to obtain a comparative performance analysis.</p>
INDEX TERMS
Data mining, association rules, similarity metric, min hashing, locality sensitive hashing.
CITATION
Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristides Gionis, Piotr Indyk, Rajeev Motwani, Jeffrey D. Ullman, Cheng Yang, "Finding Interesting Associations without Support Pruning", IEEE Transactions on Knowledge & Data Engineering, vol.13, no. 1, pp. 64-78, January/February 2001, doi:10.1109/69.908981
16 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool