期刊信息

ACM Transactions on Knowledge Discovery from Data (TKDD)

请登录查看期刊网址
免费注册：查看官网链接、跟踪截稿日期，并接收邮件提醒。
注册 登录
影响因子:
4.8
出版商:
ACM
ISSN:
1556-4681
浏览:
37831
关注:
58
这一页信息有误？告诉我们
用 API 获取这条数据
搜索与榜单列表完全无需凭证；本页的完整详情需要一把免费 API 密钥。详见开发者接入页。

征稿

ACM Transactions on Knowledge Discovery from Data (TKDD) is an academic journal published by ACM. (ISSN 1556-4681, impact factor 4.8, CCF B).

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. This journal is published nine times a year. 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. TKDD welcomes papers that both lay theoretical foundations for data mining, big data and those that provide new insights into the design and implementation of large-scale data mining systems and tools, data mining interface tools, and data mining tools that integrate with the overall information processing infrastructure. TKDD also accepts papers that describe user and data mining developer and administration experiences and issues in large-scale real-world data mining applications. The emphasis on integration of theory and practice is an attempt to encourage authors of theory papers to consider applicability and/or implementability of the theoretical results, while encouraging authors of systems papers to reflect on the theoretical results that may have been used in building the systems and/or to offer suggestions on issues that may require theoretical treatment. TKDD also solicits focused surveys on topics relevant to the publication. These should be deep and will sometimes be quite narrow, but should make a contribution to our understanding of an important area or subarea of databases. More general surveys that are intended for a broad-based Computer Science audience or surveys that may influence other areas of computing research should continue to go to ACM Computing Surveys. TKDD surveys should be educational to the database audience by presenting a relatively well-established body of database research. For additional information on the types of papers TKDD will accept, see Editorial Guidelines. The international Editorial Board is composed of recognized experts in the various subareas of this field, all with a commitment to maintain TKDD as the premier publication in this active field. The Editorial Board maintains contact with ACM's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), as well as with other societies, to encourage submission of advanced and original papers. When appropriate, concise results may be submitted as technical notes; technical comments on earlier publications are welcome as well. The existence of TKDD has helped to define the field of knowledge discovery and data mining research. It encompasses the development, formalization, and validation of abstractions and models to describe data mining applications and the design and implementation methods for knowledge discovery and automated analysis of large amount of data.
由 Dou Sun 最后更新于

相关期刊

相关会议

评论 0

暂无评论。

请登录后发表评论