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ACM Transactions on Knowledge Discovery from Data《美国计算机协会数据挖掘会报》
年9期 - 美国
  • ACM Transactions on Knowledge Discovery from Data《美国计算机协会数据挖掘会报》
  • SCIE外文期刊
  • 期发文量26
  • 国人占比73.08%
  • 维普目次
  • 投稿方式--官网投稿
  • 期刊属性

  • 中科分区:3区
    OA期刊:混合
  • 综述期刊:
    TOP期刊:
  • 期均国文:19
    环比增速:-17.39%
  • 期刊信息

  • 研究方向:计算机科学-COMPUTER SCIENCE, SOFTWARE ENGINEERING计算机:软件工程;COMPUTER SCIENCE, INFORMATION SYSTEMS计算机:信息系统
  • 国际刊号:ISSN 1556-4681;EISSN 1556-472X
  • 期刊语言:英语
    出版地区:美国
  • 投稿网址:https://mc.manuscriptcentral.com/tkdd
  • 电子邮箱:
  • 期刊官网:https://dl.acm.org/journal/tkdd
  • 作者指南:
  • 出版商网址:http://www.acm.org
  • 出版地址:ASSOC COMPUTING MACHINERY , 1601 Broadway, 10th Floor, NEW YORK, Usa, NY, 10019-7434
  • 期刊简介:ACM Transactions on Knowledge Discovery from Data《美国计算机协会数据挖掘会报》(一年9期). ACM Transactions on Knowledge Discovery from Data (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.

  • 万维提示


  • 1、投稿方式:在线投稿。

    2、期刊网址:https://dl.acm.org/journal/tkdd

    3、投稿网址:https://mc.manuscriptcentral.com/tkdd

    4、官网邮箱:tkdd-admin@acm.org

    tkdd.eic@gmail.com(主编)

    (更多编辑邮箱请查看期刊官网信息)

    5、期刊刊期:一年出版9期。

    2023727日星期四




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