Data Mining Conferences

Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [#ibmresearch]_. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is considered to be more practical and more related with real-world applications. Some good examples include recommender systems, clustering, graph mining, anomaly detection, and ensemble learning.

To facilitate KDD related research, we create this repository with:

• Upcoming data mining (DM) conference submission date, notification date, and etc.
• Historical conference acceptance rate
• Conference ranking by CORE (2018) <http://portal.core.edu.au/conf-ranks/>, Qualis (2016) <https://www.capes.gov.br/images/documentos/Qualis_periodicos_2016/Qualis_conferencia_ccomp.pdf>, CCF (2015) <https://www.ccf.org.cn/xspj/sjk/sjwj/nrjs/>_, and ERA (2012)
• Publication tips from field experts

Table of Contents\ :

• 1. 2020-2021 Data Mining Conferences_
• 2. Data Mining Conference Acceptance Rate_
• 3. Conference Ranking_
• 4. Tips for Doing Good DM Research & Get it Published!_

1. 2020-2021 Data Mining Conferences

================================================================================================= ===================== =============== ================== ================================= ============================= =========================================================================================== Conference Submission Deadline Notification Conference Date Location Acceptance Rate (2018) Website ================================================================================================= ===================== =============== ================== ================================= ============================= =========================================================================================== IEEE International Conference on Big Data (BigData) Aug 26, 2020 Oct 20, 2020 Dec 10-13, 2020 Virtual 19.7% Link <http://bigdataieee.org/BigData2020/>_ AAAI Conference on Artificial Intelligence (AAAI) Sep 01 (09), 2020 Dec 01, 2020 Feb 02-09, 2021 Virtual 20.6% Link <https://aaai.org/Conferences/AAAI-21/>_ IEEE International Conference on Data Engineering (ICDE) [Second Round] Oct 07 (14), 2020 Dec 15, 2020 Apr 19-23, 2021 Chania, Crete, Greece 18% Link <http://www.icde2021.gr/>_ SIAM International Conference on Data Mining (SDM) Sep 21, 2020 Dec TBA, 2020 Mar 25-27, 2021 Alexandria, Virginia, USA 22.9% Link <https://www.siam.org/conferences/cm/conference/sdm21>_ The Web Conference (WWW) Oct 12 (19), 2020 Jan 15, 2021 Apr 19-23, 2021 Ljubljana 15% Link <https://www2021.thewebconf.org/>_ IEEE International Conference on Data Engineering (ICDE) Oct 08 (15), 2019 Dec 14, 2019 Apr 20-24, 2020 Dallas, Texas, USA 18% Link <https://www.utdallas.edu/icde/index.html>_ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Nov 18 (25), 2019 Jan 28, 2020 May 11-14, 2020 Singapore 24.1% Link <https://www.pakdd2020.org/>_ ACM SIGKDD International Conference on Knowledge discovery and data mining (KDD) Feb 13, 2020 May 15, 2020 Aug 22-27, 2020 San Diego, California 17.8% Link <https://www.kdd.org/kdd2020/>_ European Conference on Machine learning and knowledge discovery in databases (ECML PKDD) Apr 02, 2020 Jun 04, 2020 Sep 14-18, 2020 Ghent, Belgium 25% Link <https://ecmlpkdd2020.net/>_ ACM International Conference on Information and Knowledge Management (CIKM) Apr 24 (1), 2020 Jul 03, 2020 Oct 19-23, 2020 Galway, Ireland 17% Link <https://cikm2020.org/>_ IEEE International Conference on Data Mining (ICDM) Jun 12, 2020 Aug 20, 2020 Nov 17-20, 2020 Sorrento, Italy 19.8% Link <http://icdm2020.bigke.org/>_ ACM SIGMOD/PODS Conference (SIGMOD) Jul 09, 2019 Oct 03, 2019 Jun 14-19, 2020 Portland, Oregon, USA 18% Link <https://sigmod2020.org>_ ACM International Conference on Web Search and Data Mining (WSDM) Aug 16, 2020 Oct 16, 2019 Mar 08-12, 2021 Jerusalem, Israe 16.3% Link <http://www.wsdm-conference.org/2021/>_ ================================================================================================= ===================== =============== ================== ================================= ============================= ===========================================================================================

2. Data Mining Conference Acceptance Rate

=============================================== ============================================================================================ ============================================================================== Conference Acceptance Rate Oral Presentation (otherwise poster) =============================================== ============================================================================================ ============================================================================== KDD '19 17.8% (321/1808) N/A KDD '18 18.4% (181/983, research track), 22.5% (112/497, applied data science track) 59.1% (107/181, research track), 35.7% (40/112, applied data science track) KDD '17 17.4% (130/748, research track), 22.0% (86/390, applied data science track) 49.2% (64/130, research track), 41.9% (36/86, applied data science track) KDD '16 18.1% (142/784, research track), 19.9% (66/331, applied data science track) 49.3% (70/142, research track), 60.1% (40/66, applied data science track) SDM '19 22.7% (90/397) N/A SDM '18 23.0% (86/374) N/A SDM '17 26.0% (93/358) N/A SDM '16 26.0% (96/370) N/A ICDM '19*\ 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper) N/A ICDM '18*\ 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper) N/A ICDM '17*\ 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper) N/A ICDM '16*\ 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper) N/A CIKM '19 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research) N/A CIKM '18 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper) Short papers are presented at poster sessions CIKM '17 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper) Short papers are presented at poster sessions CIKM '16 23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages) Short papers are presented at poster sessions ECML PKDD '18 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track) N/A ECML PKDD '17 28% (104/364) N/A ECML PKDD '16 28% (100/353) N/A PAKDD '19 24.1% (137/567, overall) N/A PAKDD '18 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular) N/A PAKDD '17 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular) N/A PAKDD '16 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular) N/A WSDM '19 16.4% (84/511, overall) 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^
WSDM '18 16.3% (84/514 in which 3 papers are withdrawn/rejected after the acceptance) 28.4% (23/81, long presentation), 71.6% (58/81, short presentation)^
WSDM '17 15.8% (80/505) 30% (24/80, long presentation), 70% (56/80, short presentation)^
WSDM '16 18.2% (67/368) 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^
WSDM '15 16.4% (39/238) 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^
=============================================== ============================================================================================ ==============================================================================

*\ ICDM has two tracks (regular paper track and short paper track), but the exact statistic is not released, e.g., the split between these two tracks. See ICDM Acceptance Rates <http://www.cs.uvm.edu/~icdm/ICDMAcceptanceRates.shtml>_ for more information.

^\ All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations.

Conference stats are visualized below for a straightforward comparison.

.. image:: https://github.com/yzhao062/data-mining-conferences/blob/master/conference_stats.png :target: https://github.com/yzhao062/data-mining-conferences/blob/master/conference_stats.png :alt: Conference Stats

3. Conference Ranking

================================================================================================= ===================== =============== ================== ================================= Conference CORE (2018) Qualis (2016) CCF (2019) ERA (2010) ================================================================================================= ===================== =============== ================== ================================= ACM SIGKDD International Conference on Knowledge discovery and data mining (KDD) A*\ A1 A A European Conference on Machine learning and knowledge discovery in databases (ECML PKDD) A A1 B A IEEE International Conference on Data Mining (ICDM) A*\ A1 B A SIAM International Conference on Data Mining (SDM) A A1 B A ACM International Conference on Information and Knowledge Management (CIKM) A A1 B A ACM International Conference on Web Search and Data Mining (WSDM) A*\ A1 B B Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) A A2 C A The Web Conference (WWW) A*\ A1 A A IEEE International Conference on Data Engineering (ICDE) A*\ A1 A A ================================================================================================= ===================== =============== ================== =================================

Source and ranking explanation:

• CORE (2018) <http://portal.core.edu.au/conf-ranks/>_
• Qualis (2016) <https://www.capes.gov.br/images/documentos/Qualis_periodicos_2016/Qualis_conferencia_ccomp.pdf>_
• CCF (2019) <https://www.ccf.org.cn/xspj/sjk/sjwj/nrjs/>_
• ERA (2010) <http://www.conferenceranks.com/#data>_

4. Tips for Doing Good DM Research & Get it Published!

How to do good research, Get it published in SIGKDD and get it cited! <http://www.cs.ucr.edu/~eamonn/Keogh_SIGKDD09_tutorial.pdf>_\ : a fantastic tutorial on SIGKDD'09 by Prof. Eamonn Keogh (UC Riverside).

Checklist for Revising a SIGKDD Data Mining Paper <https://web.cs.dal.ca/~eem/gradResources/KDD/Checklist%20for%20Revising%20a%20SIGKDD%20Data%20Mining%20Paper.pdf>_\ : a concise checklist by Prof. Eamonn Keogh (UC Riverside).

How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering <http://acsic.org/files/Writing16-Web.pdf>_\ : a tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette).

References

.. [#ibmresearch] IBM Research, 2018. Knowledge Discovery and Data Mining. https://researcher.watson.ibm.com/researcher/view_group.php?id=144

Last updated @ May 12th, 2019