Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports

Wanyong Tian, Fuqiang Li, Yibo Liu, Zichen Wang, Zhang Tao
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Abstract

Uncertain frequent pattern mining is usually challenged by the single probabilistic frequent threshold or the single expected support as the measurements of frequent itemsets. A promising solution based on multiple expected minimum support has been introduced in more recent studies to distinguish the mining values of each item, but the intrinsic combinatorial explosion still limited this strategy to be further improved for more generic scenarios. In this paper, a novel mining scheme for uncertain frequent itemsets is proposed. By ensembling multiple conditional item-wise supports, the problems of information redundancy as well as loss caused by a single probabilistic frequent threshold can be effectively improved. Furthermore, by using a variety of pruning strategies based on the property of sorted downward closure and the concept of least minimum probabilistic frequent threshold, an UFP-ECIS (Uncertain Frequent Pattern Mining with Ensembled Conditional Item-wise Supports) algorithm is also introduced. Substantial experiments have been proved to demonstrate that the proposed mining scheme and algorithm has enhanced the information precision of the uncertain frequent itemsets mining.
基于集成条件项支持的深度优先不确定频繁项集挖掘
不确定频繁模式挖掘通常受到单一概率频繁阈值或单一期望支持度作为频繁项集度量的挑战。在最近的研究中,提出了一种基于多个期望最小支持度的有希望的解决方案来区分每个项目的挖掘值,但固有的组合爆炸仍然限制了该策略在更一般场景下的进一步改进。提出了一种新的不确定频繁项集挖掘方案。通过集成多个条件项支持,可以有效地改善信息冗余和单个概率频繁阈值造成的损失问题。在此基础上,利用基于向下闭合排序特性和最小概率频繁阈值概念的多种剪接策略,提出了一种UFP-ECIS(不确定频繁模式挖掘与集成条件逐项支持)算法。大量实验证明,所提出的挖掘方案和算法提高了不确定频繁项集挖掘的信息精度。
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