Improving constrained pattern mining with first-fail-based heuristics

摘要

In this paper, we present a general framework to mine patterns with antimonotone constraints. This framework uses a technique that structures the pattern space in a way that facilitates the integration of constraints within the mining process. Furthermore, we also introduce a powerful strategy that uses background information on the data to speed-up the mining process. We illustrate our approach on a popular structured data mining problem, the frequent subgraph mining problem, and show, through experiments on synthetic and real-life data, that this general approach has advantages over state-of-the-art pattern mining algorithms.