.png){kind=icon}
返回
Explainable Fuzzy Utility Mining on Sequences
.png){kind=icon}
OA
AI
DOI：10.1109/TFUZZ.2021.3089284.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Fuzzy systems have good modeling capabilities in several data science scenarios and can provide human-explainable intelligence models with explainability and interpretability. To obtain a human-explainable data intelligence model for decision making, in this article, we investigate explainable fuzzy-theoretic utility mining on multisequences. 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. 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.
Keyword:
Data mining
Upper bound
Itemsets
Databases
Data models
Linguistics
Fuzzy sets
Decision making
explainability
fuzzy set
sequence
utility mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.9
论文数:
5.0K
被引数:
2.9W
机构
引用论文
分享收藏Comparison of Topical Dry Eye Medications for the Treatment of Keratoconjunctivitis Sicca in a Botulinum Toxin B-Induced Mouse Model.png)
Cornea
CorneaIF0
PREAI
分享收藏分享收藏
查看更多内容