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Mining optimized support rules for numeric attributes

机译:为数字属性挖掘优化的支持规则

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Generalizes the optimized support association rule problem by permitting rules to contain disjunctions over uninstantiated numeric attributes. For rules containing a single numeric attribute, we present a dynamic programming algorithm for computing optimized association rules. Furthermore, we propose a bucketing technique for reducing the input size, and a divide-and-conquer strategy that improves the performance significantly without sacrificing optimality. Our experimental results for a single numeric attribute indicate that our bucketing and divide-and-conquer enhancements are very effective in reducing the execution times and memory requirements of our dynamic programming algorithm. Furthermore, they show that our algorithms scale up almost linearly with the attribute's domain size as well as with the number of disjunctions.
机译:通过允许规则包含未实例化的数字属性的析取来概括优化的支持关联规则问题。对于包含单个数字属性的规则,我们提出了一种动态规划算法,用于计算优化的关联规则。此外,我们提出了一种用于减少输入大小的存储桶技术,以及一种在不牺牲最优性的前提下显着提高性能的分治策略。我们针对单个数值属性的实验结果表明,我们的存储桶和分治技术的增强功能在减少动态编程算法的执行时间和内存需求方面非常有效。此外,它们表明,我们的算法几乎与属性的域大小以及析取数成线性比例关系。

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