雜志介紹

Data Mining And Knowledge Discovery雜志介紹

《Data Mining And Knowledge Discovery》是一本以English為主的未開放獲取國際優秀期刊，中文名稱數據挖掘和知識發現，本刊主要出版、報道計算機科學-COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE領域的研究動態以及在該領域取得的各方面的經驗和科研成果，介紹該領域有關本專業的最新進展，探討行業發展的思路和方法，以促進學術信息交流，提高行業發展。該刊已被國際權威數據庫SCIE收錄，為該領域相關學科的發展起到了良好的推動作用，也得到了本專業人員的廣泛認可。該刊最新影響因子為2.8，最新CiteScore 指數為10.4。

本刊近期中國學者發表的論文主要有：

• Category tree distance: a taxonomy-based transaction distance for web user analysis

  Author: Zhang, Yinjia; Zhao, Qinpei; Shi, Yang; Li, Jiangfeng; Rao, Weixiong

• ContE: contextualized knowledge graph embedding for circular relations

  Author: Ma, Ting; Li, Mingming; Lv, Shangwen; Zhu, Fuqing; Huang, Longtao; Hu, Songlin

• Differentiated matching for individual and average treatment effect estimation

  Author: Zhao Ziyu; Kuang, Kun; Li, Bo; Cui, Peng; Wu, Runze; Xiao, Jun; Wu, Fei

• Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning

  Author: Jin, Weiqiang; Zhao, Biao; Yu, Hang; Tao, Xi; Yin, Ruiping; Liu, Guizhong

英文介紹

Data Mining And Knowledge Discovery雜志英文介紹

Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.