Abstract
Mining association rules 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,
optimized association rules 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
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