| 期刊ISSN: | 1556-4681 | ||
|---|---|---|---|
| E-ISSN: | 期刊分类: | ||
| 影响因子: | 4 | 检索数据库: | |
| 自引率: | 7.5% | 出版语言: | 英文 |
| 出版信息 | |
|---|---|
| 出版社 | Association for Computing Machinery (ACM) |
| 期刊官网 | http://tkdd.acm.org/index.html |
| 涉及的研究方向 | 计算机技术、计算机:信息系 |
| 年文章数 | 0 |
| 是否OA | 否 |
| APC费用 | |
| SCI期刊收录coverage | |
| 期刊简介 |
|---|
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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.
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| 最新中科院SCI期刊分区 (基础版) |
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| 最新中科院SCI期刊分区 (升级版) |
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