A constrained frequent pattern mining system for handling aggregate constraints

Abstract

Frequent pattern mining searches data for sets of items that are frequently co-occurring together. Most of algorithms find all the frequent patterns. However, there are many real-life situations in which users is interested in only some small portions of the entire collection of frequent patterns. To mine patterns that satisfy the user aggregate constraints in the form of agg(X.attr)θconst, properties of constraints are exploited. When agg is sum, the mining can be complicated. Existing mining systems or algorithms usually make assumptions about the value or range of X.attr and/or const. In this paper, we propose a frequent pattern mining system that avoids making these assumptions and that effectively handles the sum constraints as well as other aggregate constraints.