Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
WIREs Data Mining and Knowledge Discovery is a peer-reviewed review journal published by Wiley as part of the Wiley Interdisciplinary Reviews series, established in 2011 and devoted to data mining and knowledge discovery, with a 2025 Journal Impact Factor of 15.0(WIREs Data Mining and Knowledge Discovery)是由(Wiley)出版的同行评审综述期刊,属(Wiley Interdisciplinary Reviews)系列,2011 年创刊,专注数据挖掘与知识发现,2025 年影响因子为 15.0。
Scope
- It serves as an encyclopedic reference in data mining and knowledge discovery, capturing the latest developments from algorithmic development and applications to technologies, and commercial, legal, and ethical issues该刊是数据挖掘与知识发现领域的百科式参考期刊,涵盖从算法开发与应用到技术以及商业、法律与伦理议题的最新进展。
- It is highly interdisciplinary and explores key areas of interest to academic research, healthcare, business, and government该刊高度跨学科,探讨学术研究、医疗保健、商业与政府关注的关键领域。
- Current application areas include banking and finance, market research, risk analysis and counterterrorism, bioinformatics, medical diagnosis, epidemiology, drug discovery, environmental modeling, and meteorological data analysis当前应用领域包括银行与金融、市场研究、风险分析与反恐、生物信息学、医学诊断、流行病学、药物发现、环境建模与气象数据分析。[1]
- Review articles are invited, written, and peer-reviewed by experts in data science, computer science, machine learning, and statistics综述文章由数据科学、计算机科学、机器学习与统计学领域的专家邀约、撰写并同行评审。[2]
- The journal is indexed in the Web of Science, Scopus, and INSPEC该刊被(Web of Science)、(Scopus)与(INSPEC)收录。
- It uses eight article categories catering to readers ranging from students seeking overviews to experts needing the latest updates该刊采用八种文章类型,服务从寻求概述的学生到需要最新进展的专家等不同读者。
- Its stated objectives are to present the state of the art through ongoing reviews, capture the interdisciplinary flavor of the field, and refresh content systematically其宗旨是通过持续综述呈现领域最新水平、体现该领域的跨学科特色并系统性地更新内容。
History
- It was established in 2011 and began with Volume 1, Issue 1 in January and February of that year under Wiley-Blackwell该刊于 2011 年创刊,以当年 1 月至 2 月的第 1 卷第 1 期起步,由(Wiley-Blackwell)出版。[3]
- The founding editorial commentary was written by Editor-in-Chief Witold Pedrycz and appeared in the first issue创刊社论由主编(Witold Pedrycz)撰写,发表于创刊号。
- It belongs to the Wiley Interdisciplinary Reviews series, which combines features of encyclopedic references and review journals in an online format该刊属于(Wiley Interdisciplinary Reviews)系列,该系列以在线形式融合百科式参考与综述期刊的特点。
- Its 2025 metrics include a Journal Impact Factor of 15.0, a CiteScore of 26.6, an acceptance rate of 56%, and a median of 63 days from submission to first decision其 2025 年指标包括影响因子 15.0、(CiteScore)26.6、接受率 56% 以及投稿至初审决定中位时间 63 天。
- Research articles are published online continually, and issues now appear quarterly in March, June, September, and December研究文章持续在线发表,期次现按季度于 3 月、6 月、9 月与 12 月发行。
- It maintains a systematic program of content updates through which review articles are revised to reflect new findings该刊设有系统的内容更新机制,据此修订综述文章以反映新发现。
Editors
- Its Editor-in-Chief is Witold Pedrycz of the University of Alberta in Canada其主编为(Witold Pedrycz),任职于加拿大的(University of Alberta)。
- The editorial board includes Elisa Bertino of Purdue University in the United States编委包括美国(Purdue University)的(Elisa Bertino)。
- It also includes Frank Hoffmann of the University of Dortmund and Rudolf Kruse of the University of Magdeburg in Germany还包括(University of Dortmund)的(Frank Hoffmann)与德国(University of Magdeburg)的(Rudolf Kruse)。
- Mehmed Kantardzic of the University of Louisville and Sushmita Mitra of the Indian Statistical Institute also serve on the board(University of Louisville)的(Mehmed Kantardzic)与(Indian Statistical Institute)的(Sushmita Mitra)也在编委会任职。
- Ryszard Kowalczyk of Swinburne University of Technology and Justin Wang of La Trobe University complete the board(Swinburne University of Technology)的(Ryszard Kowalczyk)与(La Trobe University)的(Justin Wang)共同组成编委会。
- Associate editors are drawn from universities worldwide and are listed on the journal editorial board page副编辑来自全球各高校,列于该刊编委页面。
References
- ^Holzinger A, Langs G, Denk H, et al. Causability and explainability of artificial intelligence in medicine. WIREs Data Mining and Knowledge Discovery, 2019, 9: e1312. doi:10.1002/widm.1312
- ^Sagi O, Rokach L. Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery, 2018, 8: e1249. doi:10.1002/widm.1249
- ^Loh W-Y. Classification and regression trees. WIREs Data Mining and Knowledge Discovery, 2011, 1(1): 14-23. doi:10.1002/widm.8