Wiley Interdisciplinary Reviews-data Mining And Knowledge Discovery
- 期刊全称:Wiley Interdisciplinary Reviews-data Mining And Knowledge Discovery
- 简称:WIRES DATA MIN KNOWL
- ISSN:1942-4787
- ESSN:1942-4787
- 研究方向:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - COMPUTER SCIENCE, THEORY & METHODS
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Wiley Interdisciplinary Reviews-data Mining And Knowledge Discovery英文简介
The objectives of WIREs DMKD are to (a) present the current state of the art of data mining and knowledge discovery through an ongoing series of reviews written by leading researchers, (b) capture the crucial interdisciplinary flavor of the field by including articles that address the key topics from the differing perspectives of data mining and knowledge discovery, including a variety of application areas in technology, business, healthcare, education, government and society and culture, (c) capture the rapid development of data mining and knowledge discovery through a systematic program of content updates, and (d) encourage active participation in this field by presenting its achievements and challenges in an accessible way to a broad audience. The content of WIREs DMKD will be useful to upper-level undergraduate and postgraduate students, to teaching and research professors in academic programs, and to scientists and research managers in industry.
Wiley Interdisciplinary Reviews-data Mining And Knowledge Discovery中文简介
《Wiley Interdisciplinary Reviews-data Mining And Knowledge Discovery》是一本由John Wiley and Sons Inc.出版商出版的专业工程技术期刊,该刊创刊于2011年,刊期6 issues/year,该刊已被国际权威数据库SCIE收录。在中科院最新升级版分区表中,该刊分区信息为大类学科:计算机科学 2区,小类学科:计算机:理论方法 2区;计算机:人工智能 3区;在JCR(Journal Citation Reports)分区等级为Q1。该刊发文范围涵盖计算机:人工智能等领域,旨在及时、准确、全面地报道国内外计算机:人工智能工作者在该领域取得的最新研究成果、工作进展及学术动态、技术革新等,促进学术交流,鼓励学术创新。2021年影响因子为7.558,平均审稿速度>12周,或约稿。
中科院分区最新升级版(当前数据版本:2021年12月最新升级版)
大类学科 |
分区 |
小类学科 |
分区 |
Top期刊 |
综述期刊 |
计算机科学 |
2区 |
COMPUTER SCIENCE, THEORY & METHODS
计算机:理论方法
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
计算机:人工智能
|
2区
3区
|
否 |
是 |
JCR分区(当前数据版本:2021-2022年最新版)
JCR分区等级 |
JCR所属学科 |
分区 |
影响因子 |
Q1 |
COMPUTER SCIENCE, THEORY & METHODS |
Q1 |
7.558 |
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
Q1 |
期刊指数
影响因子 |
h-index |
Gold OA文章占比 |
研究类文章占比 |
OA开放访问 |
平均审稿速度 |
7.558 |
31 |
8.33% |
27.50% |
未开放 |
>12周,或约稿 |
IF值(影响因子)趋势图