Mining Informative Rule Set for Prediction

Abstract

Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this rule set informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We characterise relationships between the informative rule set and non-redundant association rule set. We present an algorithm to directly generate the informative rule set without generating all frequent itemsets first that accesses the database less frequently than other direct methods. We show experimentally that the informative rule set is much smaller and can be generated more efficiently than both the association rule set and non-redundant association rule set