雜志介紹

Acm Transactions On Knowledge Discovery From Data雜志介紹

《Acm Transactions On Knowledge Discovery From Data》是一本以English為主的未開放獲取國際優(yōu)秀期刊，中文名稱從數(shù)據(jù)中發(fā)現(xiàn)知識的 Acm 交易，本刊主要出版、報道計算機科學-COMPUTER SCIENCE, INFORMATION SYSTEMS領域的研究動態(tài)以及在該領域取得的各方面的經(jīng)驗和科研成果，介紹該領域有關本專業(yè)的最新進展，探討行業(yè)發(fā)展的思路和方法，以促進學術信息交流，提高行業(yè)發(fā)展。該刊已被國際權威數(shù)據(jù)庫SCIE收錄，為該領域相關學科的發(fā)展起到了良好的推動作用，也得到了本專業(yè)人員的廣泛認可。該刊最新影響因子為4，最新CiteScore 指數(shù)為6.7。

本刊近期中國學者發(fā)表的論文主要有：

• Integrating Global and Local Feature Selection for Multi-Label Learning

  Author: Zhang, Zan; Liu, Lin; Li, Jiuyong; Wu, Xindong

• A Self-Representation Method with Local Similarity Preserving for Fast Multi-View Outlier Detection

  Author: Wang, Yu; Chen, Chuan; Lai, Jinrong; Fu, Lele; Zhou, Yuren; Zheng, Zibin

• Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation

  Author: Wang, Ke; Zhu, Yanmin; Liu, Haobing; Zang, Tianzi; Wang, Chunyang

• Multi-Concept Representation Learning for Knowledge Graph Completion

  Author: Wang, Jiapu; Wang, Boyue; Gao, Junbin; Hu, Yongli; Yin, Baocai

英文介紹

Acm Transactions On Knowledge Discovery From Data雜志英文介紹

TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.