Declarative Heuristic Search for Pattern Set Mining

Abstract

Recently, constraint programming has been proposed as a declarative framework for constraint-based pattern mining. In constraint programming, a problem is modelled in terms of constraints and search is done by a general solver. Similar to most pattern mining algorithms, these solvers typically employ exhaustive depth-first search, where constraints are used to prune the search space and make the search viable. In this paper we investigate the use of a similar declarative approach to the problem of pattern set mining. In pattern set mining one is searching for a small and useful set of patterns. In contrast to pattern mining, however, exhaustive search is not common in pattern set mining, the search space is often far too large to make such an approach practical. In this paper, we investigate an approach which aims to make general pattern set mining feasible by using a recently developed general solver that supports exhaustive as well as heuristic search. The key idea in this solver is that next to a declarative specification of the constraints also a high-level declarative description of the search is given. By separating the model and the search from the solver, the approach offers the advantage of reusing constraints and search strategies declaratively, while also allowing fast heuristic search.