会议信息
KDD 2024: ACM SIGKDD Conference on Knowledge Discovery and Data Mining
https://kdd2024.kdd.org/截稿日期: |
2024-08-01 |
通知日期: |
|
会议日期: |
2024-08-25 |
会议地点: |
Barcelona, Spain |
届数: |
30 |
CCF: a CORE: a* QUALIS: a1 浏览: 217899 关注: 516 参加: 53
征稿
Scope For the research track, we invite submission of papers describing innovative research on all aspects of knowledge discovery and data science, ranging from theoretical foundations to novel models and algorithms for data science problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Topics of interest include, but are not limited to: Data Science: Methods for analyzing social networks, time series, sequences, streams, text, web, graphs, rules, patterns, logs, IoT data, spatio-temporal data, biological data, scientific and business data; recommender systems, computational advertising, multimedia, finance, bioinformatics. Big Data: Large-scale systems for data analysis, machine learning, optimization, sampling, summarization; parallel and distributed data science (cloud, map-reduce, federated learning); novel algorithmic and statistical techniques for big data; algorithmically-efficient data transformation and integration. Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, transfer learning, representation learning, meta learning, reinforcement learning; classification, clustering, regression, semi-supervised learning, self-supervised learning, few shot learning and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability, ethics and robustness.
最后更新 Dou Sun 在 2024-03-24
录取率
时间 | 提交数 | 录取数 | 录取率(%) |
---|---|---|---|
2018 | 983 | 107 | 10.9% |
2017 | 748 | 64 | 8.6% |
2016 | 1115 | 66 | 5.9% |
2015 | 819 | 160 | 19.5% |
2014 | 1036 | 151 | 14.6% |
2013 | 726 | 125 | 17.2% |
2012 | 755 | 133 | 17.6% |
2011 | 714 | 126 | 17.6% |
2010 | 578 | 101 | 17.5% |
2009 | 537 | 105 | 19.6% |
2008 | 593 | 118 | 19.9% |
2007 | 573 | 111 | 19.4% |
2006 | 457 | 50 | 10.9% |
2005 | 358 | 76 | 21.2% |
2004 | 337 | 40 | 11.9% |
2003 | 258 | 34 | 13.2% |
2001 | 203 | 52 | 25.6% |
2000 | 246 | 50 | 20.3% |
最佳论文
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相关期刊
CCF | 全称 | 影响因子 | 出版商 | ISSN |
---|---|---|---|---|
a | IEEE Transactions on Knowledge and Data Engineering | 3.857 | IEEE | 1041-4347 |
b | Data Mining and Knowledge Discovery | 3.670 | Springer | 1384-5810 |
b | Data & Knowledge Engineering | 1.992 | Elsevier | 0169-023X |
Statistical Analysis and Data Mining | John Wiley & Sons, Ltd | 1932-1872 | ||
Knowledge Engineering Review | Cambridge University Press | 0269-8889 | ||
BioData Mining | 2.522 | Springer | 1756-0381 | |
c | International Journal of Software Engineering and Knowledge Engineering | World Scientific | 0218-1940 | |
b | Knowledge and Information Systems | 2.822 | Springer | 0219-1377 |
c | International Journal of Knowledge Management | IGI Global | 1548-0666 | |
c | IEEE Signal Processing Letters | 3.268 | IEEE | 1070-9908 |
全称 | 影响因子 | 出版商 |
---|---|---|
IEEE Transactions on Knowledge and Data Engineering | 3.857 | IEEE |
Data Mining and Knowledge Discovery | 3.670 | Springer |
Data & Knowledge Engineering | 1.992 | Elsevier |
Statistical Analysis and Data Mining | John Wiley & Sons, Ltd | |
Knowledge Engineering Review | Cambridge University Press | |
BioData Mining | 2.522 | Springer |
International Journal of Software Engineering and Knowledge Engineering | World Scientific | |
Knowledge and Information Systems | 2.822 | Springer |
International Journal of Knowledge Management | IGI Global | |
IEEE Signal Processing Letters | 3.268 | IEEE |
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