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README.rst update KDD and PKDD 2020 Nov 12, 2019
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README.rst

Data Mining Conferences


Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1]. 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), Qualis (2016), CCF (2015), and ERA (2012)
  • Publication tips from field experts

Table of Contents:


1. 2019-2020 Data Mining Conferences

Conference Submission Deadline Notification Conference Date Location Acceptance Rate (2018) Website
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
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
European Conference on Machine learning and knowledge discovery in databases (ECML PKDD) Mar 19 (26), 2020 Jun 04, 2020 Sep 14-18, 2020 Ghent, Belgium 25% Link
ACM International Conference on Information and Knowledge Management (CIKM) May 15, 2019 Aug 06, 2019 Nov 03-07,2019 Beijing, China 17% Link
IEEE International Conference on Data Mining (ICDM) Jun 05, 2019 Aug 08, 2019 Nov 08-11, 2019 Beijing, China 19.8% Link
IEEE International Conference on Data Engineering (ICDE) [First Round] Jun 08, 2019 Aug 10, 2019 Apr 20-24, 2020 Dallas, Texas, USA 18% Link
ACM SIGMOD/PODS Conference (SIGMOD) Jul 09, 2019 Oct 03, 2019 Jun 14-19, 2020 Portland, Oregon, USA 18% Link
ACM International Conference on Web Search and Data Mining (WSDM) Aug 12, 2019 Oct 12, 2019 Feb 05-09, 2020 Houston, Texas, USA 16.3% Link
IEEE International Conference on Big Data (BigData) Aug 19, 2019 Oct 16, 2019 Dec 09-12, 2019 Log Angels, CA, USA 19.7% Link
SIAM International Conference on Data Mining (SDM) Oct 04 (11), 2019 Dec, 2019 (TBA) May 05-07, 2020 Cincinnati, Ohio, USA 22.9% Link
The Web Conference (WWW) Oct 07 (14), 2019 Jan 10, 2020 Apr 20-24, 2020 Taipei, Taiwan 15% Link
IEEE International Conference on Data Engineering (ICDE) [Second Round] Oct 08 (15), 2019 Dec 14, 2019 Apr 20-24, 2020 Dallas, Texas, USA 18% Link

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 '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 '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 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.

Conference Stats

3. Conference Ranking

Conference CORE (2018) Qualis (2016) CCF (2015) 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:


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

How to do good research, Get it published in SIGKDD and get it cited!: a fantastic tutorial on SIGKDD'09 by Prof. Eamonn Keogh (UC Riverside).

Checklist for Revising a SIGKDD Data Mining Paper: a concise checklist by Prof. Eamonn Keogh (UC Riverside).

How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering: a tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette).


References

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

Last updated @ May 12th, 2019

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