基于效用偏差的多最小效用阈值高效用项集挖掘

Naji Alhusaini, Jing Li, Philippe Fournier-Viger, Ammar Hawbani, Guilin Chen
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摘要

高效用项集挖掘(HUIM)是一项考虑利润和数量等项的效用提取可操作模式的任务。传统HUIM方法的一个重要问题是,它们使用单一阈值来评估所有项目,由于项目的性质和重要性的差异,这与现实不一致。最近提出了一种算法,通过为每个项目分配最小项目效用阈值来解决这个问题。然而,由于最小项目效用(MIU)是用外部效用的百分比表示的,这些方法仍然面临两个问题,称为“项目集缺失”和“项目集爆炸”。为了解决这些问题,本文引入了基于标准偏差计算的效用偏差的新概念。在计算项目的MIU时,采用了U D和实际效用相结合的方法。这样可以缓解“物品集缺失”和“物品集爆炸”的问题。为了实现和评估U- D概念,提出了一种新的算法,称为HUI-MMU-UD。实验结果证明了该方法在解决“项集缺失”和“项集爆炸”问题上的有效性。结果还表明,在运行时间和内存使用方面,该算法在许多情况下都优于以前的HUI-MMU算法。
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Mining High Utility Itemset with Multiple Minimum Utility Thresholds Based on Utility Deviation
High Utility Itemset Mining (HUIM) is the task of extracting actionable patterns considering the utility of items such as profits and quantities. An important issue with traditional HUIM methods is that they evaluate all items using a single threshold, which is inconsistent with reality due to differences in the nature and importance of items. Recently, algorithms were proposed to address this problem by assigning a minimum item utility threshold to each item. However, since the minimum item utility (MIU) is expressed as a percentage of the external utility, these methods still face two problems, called “itemset missing” and “itemset explosion”. To solve these problems, this paper introduces a novel notion of Utility Deviation (UD), which is calculated based on the standard deviation. The U D and actual utility are jointly used to calculate the MIU of items. By doing so, the problems of “itemset missing” and “itemset explosion” are alleviated. To implement and evaluate the U D notion, a novel algorithm is proposed, called HUI-MMU-UD. Experimental results demonstrate the effectiveness of the proposed notion for solving the problems of “itemset missing” and “itemset explosion”. Results also show that the proposed algorithm outperforms the previous HUI-MMU algorithm in many cases, in terms of runtime and memory usage.
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