Mining frequent binary expressions

In data mining, searching for frequent patterns is a common basic operation. It forms the basis of many interesting decision support processes. In this paper we present a new type of patterns, binary expressions. Based on the properties of a specified binary test, such as reflexivity, transitivity and symmetry, we construct a generic algorithm that mines all frequent binary expressions. We present three applications of this new type of expressions: mining for rules, for horizontal decompositions, and in intensional database relations. Since the number of binary expressions can become exponentially large, we use data mining techniques to avoid exponential execution times. We present results of the algorithm that show an exponential gain in time due to a well chosen pruning technique.