序列上可解释的模糊效用挖掘
Explainable Fuzzy Utility Mining on Sequences
摘要：
模糊系统在几种数据科学场景中具有良好的建模能力，并且可以提供具有可解释性和可解释性的人类可解释的智能模型。与已被广泛研究的事务数据相比，序列数据在实际应用中更常见。为了获得一个人类可解释的决策数据智能模型，在这项研究中，我们研究了可解释的模糊理论效用挖掘多序列。同时，给出了序列上模糊效用挖掘问题的一个更规范的形式化描述。通过对效用挖掘的模糊集理论的探索，提出了一种新的挖掘具有语言意义的模糊高效用序列的方法--模式增长模糊效用挖掘（PGFUM）。在序列数据的情况下，PGFUM反映了序列的模糊量和效用域。为了提高PGFUM的效率和可行性，我们开发了两种压缩的数据结构与可解释的模糊性。此外，一个现有的和两个新的上界的解释模糊效用的候选人采用三个建议的修剪策略，大大减少了搜索空间，从而加快挖掘过程。最后，通过大量的实验评估，将所提出的PGFUM算法与PFUS进行了比较，PFUS是目前唯一可用的用于相同任务的方法。结果表明，PGFUM不仅实现了人类可解释的挖掘结果，包含原始性质的可揭示的可理解性，但在运行时间和内存成本方面也很高的效率。
英文摘要：
Fuzzy systems have good modeling capabilities in several data science scenarios, and can provide human-explainable intelligence models with explainability and interpretability. In contrast to transaction data, which have been extensively studied, sequence data are more common in real-life applications. To obtain a human-explainable data intelligence model for decision making, in this study, we investigate explainable fuzzy-theoretic utility mining on multi-sequences. Meanwhile, a more normative formulation of the problem of fuzzy utility mining on sequences is formulated. By exploring fuzzy set theory for utility mining, we propose a novel method termed pattern growth fuzzy utility mining (PGFUM) for mining fuzzy high-utility sequences with linguistic meaning. In the case of sequence data, PGFUM reflects the fuzzy quantity and utility regions of sequences. To improve the efficiency and feasibility of PGFUM, we develop two compressed data structures with explainable fuzziness. Furthermore, one existing and two new upper bounds on the explainable fuzzy utility of candidates are adopted in three proposed pruning strategies to substantially reduce the search space and thus expedite the mining process. Finally, the proposed PGFUM algorithm is compared with PFUS, which is the only currently available method for the same task, through extensive experimental evaluation. It is demonstrated that PGFUM achieves not only human-explainable mining results that contain the original nature of revealable intelligibility, but also high efficiency in terms of runtime and memory cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
