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ACM Transactions on Knowledge Discovery from Data 中科院3区 JCR:Q1 SCIE EI PubMed JST
发文量 1,416
被引量 44,574
影响因子(2025版) 3.532

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.

  • 主办单位: ASSOC COMPUTING MACHINERY
  • 出版地区: NEW YORK
  • 出版周期: 季刊
  • 别名: TKDD;ACM T KNOWL DISCOV D;ACM Trans. Knowl. Discov. Data;美国计算机学会数据知识挖掘汇刊;ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
  • 国际标准连续出版物号/电子版 ISSN 1556-4681 / EISSN 1556-472X
  • 创刊时间: 2007年
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