Exploratory mining and pruning optimizations of constrained associations rules

TLDR

Current association rule mining models lack user exploration, focus, and flexible relationships, functioning as black-boxes with minimal interaction. The paper proposes an architecture that opens the black-box to enable constraint‑based, human‑centered exploratory mining of associations. The architecture relies on a rich set of constraint constructs—domain, class, and SQL‑style aggregate constraints—and introduces constrained association queries to define antecedent and consequent requirements.

Abstract

From the standpoint of supporting human-centered discovery of knowledge, the present-day model of mining association rules suffers from the following serious shortcomings: (i) lack of user exploration and control, (ii) lack of focus, and (iii) rigid notion of relationships. In effect, this model functions as a black-box, admitting little user interaction in between. We propose, in this paper, an architecture that opens up the black-box, and supports constraint-based, human-centered exploratory mining of associations. The foundation of this architecture is a rich set of constraint constructs, including domain, class, and SQL-style aggregate constraints, which enable users to clearly specify what associations are to be mined. We propose constrained association queries as a means of specifying the constraints to be satisfied by the antecedent and consequent of a mined association.