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The Efficient Mining of Skyline Patterns from a Volunteer Computing Network
ACM Transactions on Internet Technology ( IF 3.9 ) Pub Date : 2021-07-16 , DOI: 10.1145/3423557
Jimmy Ming-Tai Wu, Qian Teng, Gautam Srivastava, Matin Pirouz, Jerry Chun-Wei Lin

In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspects of frequency and utility has been extensively discussed. In most cases, however, people tend to focus on purchase quantities of itemsets rather than frequencies. In this article, we propose a new knowledge called skyline quantity-utility pattern (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms, respectively, called SQU-Miner and SKYQUP are presented to efficiently mine the set of SQUPs. Moreover, the usage of volunteer computing is proposed to show the potential in real supermarket applications. Two new efficient utility-max structures are also mentioned for the reduction of the candidate itemsets, respectively, utilized in SQU-Miner and SKYQUP. These two new utility-max structures are used to store the upper-bound of utility for itemsets under the quantity constraint instead of frequency constraint, and the second proposed utility-max structure moreover applies a recursive updated process to further obtain strict upper-bound of utility. Our in-depth experimental results prove that SKYQUP has stronger performance when a comparison is made to SQU-Miner in terms of memory usage, runtime, and the number of candidates.

中文翻译:

从志愿计算网络中高效挖掘天际线图案

在不断发展的世界中,高效用项集挖掘 (HUIM) 和频繁项集挖掘 (FIM) 的概念是知识发现的基础工作。已经成功设计了几种算法。然而,这些算法只使用一个因子来估计一个项集。过去,通过考虑频率和效用两方面的天际线模式挖掘已被广泛讨论。然而,在大多数情况下,人们倾向于关注项目集的购买数量而不是频率。在本文中,我们提出了一种称为天际线数量效用模式(SQUP)的新知识,通过同时考虑数量和效用,在决策过程中提供更好的估计。提出了两种算法,分别称为 SQU-Miner 和 SKYQUP,以有效地挖掘 SQUP 集。而且,提议使用志愿者计算来展示实际超市应用的潜力。还提到了两个新的有效效用最大值结构,分别用于 SQU-Miner 和 SKYQUP 中用于减少候选项目集。这两个新的效用最大值结构用于存储数量约束而不是频率约束下项集的效用上限,并且第二个提出的效用最大值结构还应用递归更新过程来进一步获得严格的上限效用。我们深入的实验结果证明,与 SQU-Miner 相比,SKYQUP 在内存使用、运行时间和候选数量方面具有更强的性能。还提到了两个新的有效效用最大值结构,分别用于 SQU-Miner 和 SKYQUP 中用于减少候选项目集。这两个新的效用最大值结构用于存储数量约束而不是频率约束下项集的效用上限,并且第二个提出的效用最大值结构还应用递归更新过程来进一步获得严格的上限效用。我们深入的实验结果证明,与 SQU-Miner 相比,SKYQUP 在内存使用、运行时间和候选数量方面具有更强的性能。还提到了两个新的有效效用最大值结构,分别用于 SQU-Miner 和 SKYQUP 中用于减少候选项目集。这两个新的效用最大值结构用于存储数量约束而不是频率约束下项集的效用上限,并且第二个提出的效用最大值结构还应用递归更新过程来进一步获得严格的上限效用。我们深入的实验结果证明,与 SQU-Miner 相比,SKYQUP 在内存使用、运行时间和候选数量方面具有更强的性能。这两个新的效用最大值结构用于存储数量约束而不是频率约束下项集的效用上限,并且第二个提出的效用最大值结构还应用递归更新过程来进一步获得严格的上限效用。我们深入的实验结果证明,与 SQU-Miner 相比,SKYQUP 在内存使用、运行时间和候选数量方面具有更强的性能。这两个新的效用最大值结构用于存储数量约束而不是频率约束下项集的效用上限,并且第二个提出的效用最大值结构还应用递归更新过程来进一步获得严格的上限效用。我们深入的实验结果证明,与 SQU-Miner 相比,SKYQUP 在内存使用、运行时间和候选数量方面具有更强的性能。
更新日期:2021-07-16
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