The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - January/February (2002 vol.14)
pp: 29-50
ABSTRACT
<p><b>Abstract</b>—Mining <it>association rules</it> on large data sets has received considerable attention in recent years. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial, and retail sectors. Furthermore, <it>optimized association rules</it> are an effective way to focus on the most interesting characteristics involving certain attributes. Optimized association rules are permitted to contain uninstantiated attributes and the problem is to determine instantiations such that either the support or confidence of the rule is maximized. In this paper, we generalize the optimized association rules problem in three ways: 1) association rules are allowed to contain disjunctions over uninstantiated attributes, 2) association rules are permitted to contain an arbitrary number of uninstantiated attributes, and 3) uninstantiated attributes can be either categorical or numeric. Our generalized association rules enable us to extract more useful information about seasonal and local patterns involving multiple attributes. We present effective techniques for pruning the search space when computing optimized association rules for both categorical and numeric attributes. Finally, we report the results of our experiments that indicate that our pruning algorithms are efficient for a large number of uninstantiated attributes, disjunctions, and values in the domain of the attributes.</p>
INDEX TERMS
data mining, knowledge discovery, optimized association rules, algorithm
CITATION
R. Rastogi, "Mining Optimized Association Rules with Categorical and Numeric Attributes", IEEE Transactions on Knowledge & Data Engineering, vol.14, no. 1, pp. 29-50, January/February 2002, doi:10.1109/69.979971
239 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool