# data-mining-conferences
**Repository Path**: mirrors_jmrozanec/data-mining-conferences
## Basic Information
- **Project Name**: data-mining-conferences
- **Description**: Ranking, acceptance rate, deadline, and publication tips
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-21
- **Last Updated**: 2026-01-17
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
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) `_, `Qualis (2016) `_, `CCF (2015) `_, 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 `_
AAAI Conference on Artificial Intelligence (**AAAI**) **Sep 01 (09), 2020** Dec 01, 2020 Feb 02-09, 2021 Virtual 20.6% `Link `_
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 `_
SIAM International Conference on Data Mining (**SDM**) **Sep 21, 2020** Dec TBA, 2020 Mar 25-27, 2021 Alexandria, Virginia, USA 22.9% `Link `_
The Web Conference (**WWW**) **Oct 12 (19), 2020** Jan 15, 2021 Apr 19-23, 2021 Ljubljana 15% `Link `_
IEEE International Conference on Data Engineering (**ICDE**) Oct 08 (15), 2019 Dec 14, 2019 Apr 20-24, 2020 Dallas, Texas, USA 18% `Link `_
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**) Apr 02, 2020 Jun 04, 2020 Sep 14-18, 2020 Ghent, Belgium 25% `Link `_
ACM International Conference on Information and Knowledge Management (**CIKM**) Apr 24 (1), 2020 Jul 03, 2020 Oct 19-23, 2020 Galway, Ireland 17% `Link `_
IEEE International Conference on Data Mining (**ICDM**) Jun 12, 2020 Aug 20, 2020 Nov 17-20, 2020 Sorrento, Italy 19.8% `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 16, 2020** Oct 16, 2019 Mar 08-12, 2021 Jerusalem, Israe 16.3% `Link `_
================================================================================================= ===================== =============== ================== ================================= ============================= ===========================================================================================
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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 `_ 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) `_
* `Qualis (2016) `_
* `CCF (2019) `_
* `ERA (2010) `_
----
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
----------
.. [#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