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Mining optimized support rules for numeric attributes

机译:挖掘数字属性的优化支持规则

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In this paper, we generalize 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 programmingalgorithm 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 experimentalresults 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 ouralgorithms scale up almost linearly with the attribute's domain size as well as the number of disjunctions.
机译:在本文中,我们概括了通过允许规则包含不稳定的数字属性的剖钉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编程,用于计算优化关联规则。此外,我们提出了一种减少输入尺寸的铲斗技术,以及划分和征服策略,可以显着提高性能而不会牺牲最优性。我们的单一数字属性的实验结果表明我们的铲斗和分割和征服增强功能非常有效地减少我们动态编程算法的执行时间和内存要求。此外,他们表明OuroToRithms几乎线性地缩放了属性的域大小以及剖钉的数量。

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