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Optimizing Constraint-Based Mining by Automatically Relaxing Constraints

机译:通过自动放松约束优化基于约束的挖掘

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In constraint-based mining, the monotone and anti-monotone properties are exploited to reduce the search space. Even if a constraint has not such suitable properties, existing algorithms can be re-used thanks to an approximation, called relaxation. In this paper, we automatically compute monotone relaxations of primitive-based constraints. First, we show that the latter are a superclass of combinations of both kinds of monotone constraints. Second, we add two operators to detect the properties of monotonicity of such constraints. Finally, we define relaxing operators to obtain monotone relaxations of them.
机译:在基于约束的挖掘中,利用单调和抗单调属性来减少搜索空间。即使约束没有这样合适的属性,由于近似,可以重新使用现有算法,称为松弛。在本文中,我们自动计算基于原始的约束的单调放松。首先,我们表明后者是两种单调约束的组合的超类。其次,我们添加了两个运营商来检测这种约束的单调性的属性。最后,我们定义了放松的运营商,以获得它们的单调放松。

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