Knowledge Discovery and Data Mining (KDD) is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data [1]. Different from machine learning, KDD is considered to be more practical and more closely tied with real-world applications.
To facilitate KDD related research, we create this repository with:
- Incoming data mining conference submission date, notification date, and more
- Historical acceptance rate
- Tips for publications
| Conference | Submission Deadline | Notification | Conference Date | Location | Acceptance Rate (2018) | Website |
|---|---|---|---|---|---|---|
| ACM SIGKDD International Conference on Knowledge discovery and data mining (KDD) | Feb 09, 2019 | Apr 28, 2019 | Aug 04-08, 2019 | Anchorage, Alaska, USA | 18.3% (research) & 22.5% (ds) | Link |
| European Conference on Machine learning and knowledge discovery in databases (ECML PKDD) | Apr 05, 2019 | Jun 07, 2019 | Sep 16-20, 2019 | Würzburg, Germany | 25% | Link |
| IEEE International Conference on Data Mining (ICDM) | Jun 05, 2019 | Aug 08, 2019 | Nov 08-11, 2019 | Beijing, China | 19.8% | Link |
| SIAM International Conference on Data Mining (SDM) | TBA | TBA | TBA | TBA | 22.9% | TBA |
| ACM International Conference on Information and Knowledge Management (CIKM) | TBA | TBA | TBA | TBA | 17% | TBA |
| ACM International Conference on Web Search and Data Mining (WSDM) | TBA | TBA | TBA | TBA | 16.3% | TBA |
| Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) | TBA | TBA | TBA | TBA | TBA | TBA |
| The Web Conference (WWW) | TBA | TBA | TBA | Taiwan | 15% | TBA |
| IEEE International Conference on Data Engineering (ICDE) | TBA | TBA | TBA | TBA | 18% | TBA |
| Conference | Acceptance Rate | Oral Presentation (otherwise poster) |
|---|---|---|
| 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 |
*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.
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).
| [1] | IBM Research, 2018. Knowledge Discovery and Data Mining. https://researcher.watson.ibm.com/researcher/view_group.php?id=144 |