Enumerating minimal generators from closed itemsets-toward effective compression of negative association rules
Enumerating minimal generators from closed itemsets-toward effective compression of negative association rules
复制标题
从闭项集中枚举最小生成器——实现负关联规则的有效压缩
DOI：
10.1109/csde53843.2021.9718380
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Yoshitaka Yamamoto
中科院分区：
文献类型：
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作者：
Koji Iwanuma;Kento Yajima;Yoshitaka Yamamoto
负关联规则是表达隐藏在大数据中的各种潜在属性的重要工具。然而，有效的负关联规则的数量往往会变得非常庞大，因此一种有效的负关联规则集压缩方法就显得非常重要。最小生成元对于压缩有效否定规则集非常有用。本文研究了从给定的闭项集中枚举最小生成元的几种有效算法。特别地，我们提出了一种新的枚举算法，它不使用任何支持计算，但使用一个自顶向下的树搜索的渴望哈希搜索。我们的枚举算法进行评估的实验结果，并确认非常好的性能的枚举方法没有支持计算。
Negative association rules are valuable and essential for expressing various latent properties which hide in big data. The number of valid negative association rules, however, always becomes so huge, thus an effective compression method of the set of negative rules is quite important. Minimal generators are very useful for compressing the set of valid negative rules. In this paper, we study several efficient algorithms for enumerating minimal generators from given closed itemsets. Especially we propose a novel enumeration algorithm which does not use any support computation, but uses an eager hash search in a top-down tree search. We show experimental results for evaluating our enumeration algorithms, and confirm very good performance of the enumeration method without support computation.