Mining constraint-based patterns using automatic relaxation

Constraint-based mining is an active field of research which is a necessary step to achieve interactive and successful KDD processes. The limitations of the task lies in languages being limited to describe the mined patterns and the ability to express varied constraints. In practice, current approaches focus on a language and the most generic frameworks mine individually or simultaneously a monotone and an anti-monotone constraints. In this paper, we propose a generic framework dealing with any partially ordered language and a large set of constraints. We prove that this set of constraints called primitive-based constraints not only is a superclass of both kinds of monotone ones and their boolean combinations but also other classes such as convertible and succinct constraints. We show that the primitive-based constraints can be efficiently mined thanks to a relaxation method based on virtual patterns which summarize the specificities of the search space. Indeed, this approach automatically deduces pruning conditions having suitable monotone properties and thus these conditions can be pushed into usual constraint mining algorithms. We study the optimal relaxations. Finally, we provide an experimental illustration of the efficiency of our proposal by experimenting it on several contexts.

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