Searching Frequent Pattern and Prefix Trees for Higher Order Rules

Since the search for rules that can inform business decision making is the ultimate goal of data mining technology, problems such as the interpretation of interestingness for discovered rules is an important issue. However, the search for rules that adhere to a user's definition of interesting remains somewhat elusive, in part because rules are commonly supplied in a low, instance-level format. In this paper we argue that rules with more useable semantics can be obtained by searching for patterns in the intermediate data structures such as frequent pattern or prefix trees. This paper discusses this approach and present a proof-of-concept system, Horace, that shows that the approach is both useable and efficient.