Conference Information
KDD 2020: ACM SIGKDD Conference on Knowledge Discovery and Data Mining
https://www.kdd.org/kdd2020/
Submission Date:
2020-02-13
Notification Date:
2020-05-15
Conference Date:
2020-08-22
Location:
San Diego, California, USA
Years:
26
CCF: a   CORE: a*   QUALIS: a1   Viewed: 106830   Tracked: 361   Attend: 31

Conference Location
Call For Papers
We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining, ranging from theoretical foundations to novel models and algorithms for data mining 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. Authors are explicitly discouraged from submitting incremental results that do not provide major advances over existing approaches.

Topics of interest include, but are not limited to:

● Data Science: Methods for analyzing scientific and business data, social networks, time series; mining sequences, streams, text, web, graphs, rules, patterns, logs data, IoT data, spatio-temporal data, biological data; recommender systems, computational advertising, multimedia, finance, bioinformatics.

● Big Data: Large-scale systems for text and graph analysis, machine learning, optimization, sampling, parallel and distributed data mining (cloud, map-reduce, federated learning), novel algorithmic and statistical techniques for big data.

● Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, meta learning, AutoML, reinforcement learning; classification, clustering, regression, semi-supervised and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability and robustness.  
Last updated by Dou Sun in 2019-11-19
Acceptance Ratio
YearSubmittedAcceptedAccepted(%)
201898310710.9%
2017748648.6%
20161115665.9%
201581916019.5%
2014103615114.6%
201372612517.2%
201275513317.6%
201171412617.6%
201057810117.5%
200953710519.6%
200859311819.9%
200757311119.4%
20064575010.9%
20053587621.2%
20043374011.9%
20032583413.2%
20012035225.6%
20002465020.3%
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Related Journals
CCFFull NameImpact FactorPublisherISSN
aIEEE Transactions on Knowledge and Data Engineering3.857IEEE1041-4347
bData Mining and Knowledge Discovery3.16Springer1384-5810
bData & Knowledge Engineering1.583Elsevier0169-023X
Statistical Analysis and Data Mining John Wiley & Sons, Ltd1932-1872
Knowledge Engineering ReviewCambridge University Press0269-8889
BioData Mining2.301Springer1756-0381
cInternational Journal of Software Engineering and Knowledge Engineering World Scientific0218-1940
bKnowledge and Information Systems2.008Springer0219-1377
cInternational Journal of Knowledge Management IGI Global1548-0666
cIEEE Signal Processing Letters3.268IEEE1070-9908
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