{"id":16891791,"url":"https://github.com/yzhao062/data-mining-conferences","last_synced_at":"2025-10-04T06:09:25.297Z","repository":{"id":53542512,"uuid":"167444995","full_name":"yzhao062/data-mining-conferences","owner":"yzhao062","description":"Ranking, acceptance rate, deadline, and publication tips","archived":false,"fork":false,"pushed_at":"2021-03-25T10:46:38.000Z","size":647,"stargazers_count":334,"open_issues_count":2,"forks_count":40,"subscribers_count":28,"default_branch":"master","last_synced_at":"2025-05-20T09:02:13.177Z","etag":null,"topics":["data-mining","data-science","research"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/yzhao062.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-01-24T22:04:30.000Z","updated_at":"2025-04-22T14:06:07.000Z","dependencies_parsed_at":"2022-08-24T21:30:56.180Z","dependency_job_id":null,"html_url":"https://github.com/yzhao062/data-mining-conferences","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/yzhao062/data-mining-conferences","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yzhao062%2Fdata-mining-conferences","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yzhao062%2Fdata-mining-conferences/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yzhao062%2Fdata-mining-conferences/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yzhao062%2Fdata-mining-conferences/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yzhao062","download_url":"https://codeload.github.com/yzhao062/data-mining-conferences/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yzhao062%2Fdata-mining-conferences/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278272833,"owners_count":25959654,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-04T02:00:05.491Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-mining","data-science","research"],"created_at":"2024-10-13T17:08:42.667Z","updated_at":"2025-10-04T06:09:25.270Z","avatar_url":"https://github.com/yzhao062.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"Data Mining Conferences\n=======================\n\n----\n\n**Knowledge Discovery and Data Mining** is an interdisciplinary area focusing\nupon methodologies and applications for extracting useful knowledge from data [#ibmresearch]_.\nDifferent from machine learning, Knowledge Discovery and Data Mining (KDD) is\nconsidered to be more practical and more related with real-world applications.\nSome good examples include recommender systems, clustering, graph mining,\nanomaly detection, and ensemble learning.\n\nTo facilitate KDD related research, we create this repository with:\n\n* **Upcoming data mining (DM) conference** submission date, notification date, and etc.\n* **Historical conference acceptance rate**\n* **Conference ranking** by `CORE (2018) \u003chttp://portal.core.edu.au/conf-ranks/\u003e`_, `Qualis (2016) \u003chttps://www.capes.gov.br/images/documentos/Qualis_periodicos_2016/Qualis_conferencia_ccomp.pdf\u003e`_, `CCF (2015) \u003chttps://www.ccf.org.cn/xspj/sjk/sjwj/nrjs/\u003e`_, and ERA (2012)\n* **Publication tips** from field experts\n\n\n**Table of Contents**\\ :\n\n* `1. 2020-2021 Data Mining Conferences`_\n* `2. Data Mining Conference Acceptance Rate`_\n* `3. Conference Ranking`_\n* `4. Tips for Doing Good DM Research \u0026 Get it Published!`_\n\n\n----\n\n\n1. 2020-2021 Data Mining Conferences\n------------------------------------\n\n\n=================================================================================================  =====================  ===============  ==================  =================================  =============================  ===========================================================================================\nConference                                                                                         Submission Deadline    Notification     Conference Date     Location                           Acceptance Rate (2018)         Website\n=================================================================================================  =====================  ===============  ==================  =================================  =============================  ===========================================================================================\nIEEE International Conference on Big Data (**BigData**)                                            **Aug 26, 2020**       Oct 20, 2020     Dec 10-13, 2020     Virtual                            19.7%                          `Link \u003chttp://bigdataieee.org/BigData2020/\u003e`_\nAAAI Conference on Artificial Intelligence (**AAAI**)                                              **Sep 01 (09), 2020**  Dec 01, 2020     Feb 02-09, 2021     Virtual                            20.6%                          `Link \u003chttps://aaai.org/Conferences/AAAI-21/\u003e`_\nIEEE International Conference on Data Engineering (**ICDE**) [**Second Round**]                    **Oct 07 (14), 2020**  Dec 15, 2020     Apr 19-23, 2021     Chania, Crete, Greece              18%                            `Link \u003chttp://www.icde2021.gr/\u003e`_\nSIAM International Conference on Data Mining (**SDM**)                                             **Sep 21, 2020**       Dec TBA, 2020    Mar 25-27, 2021     Alexandria, Virginia, USA          22.9%                          `Link \u003chttps://www.siam.org/conferences/cm/conference/sdm21\u003e`_\nThe Web Conference (**WWW**)                                                                       **Oct 12 (19), 2020**  Jan 15, 2021     Apr 19-23, 2021     Ljubljana                          15%                            `Link \u003chttps://www2021.thewebconf.org/\u003e`_\nIEEE International Conference on Data Engineering (**ICDE**)                                       Oct 08 (15), 2019      Dec 14, 2019     Apr 20-24, 2020     Dallas, Texas, USA                 18%                            `Link \u003chttps://www.utdallas.edu/icde/index.html\u003e`_\nPacific-Asia Conference on Knowledge Discovery and Data Mining (**PAKDD**)                         Nov 18 (25), 2019      Jan 28, 2020     May 11-14, 2020     Singapore                          24.1%                          `Link \u003chttps://www.pakdd2020.org/\u003e`_\nACM 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 \u003chttps://www.kdd.org/kdd2020/\u003e`_\nEuropean 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 \u003chttps://ecmlpkdd2020.net/\u003e`_\nACM International Conference on Information and Knowledge Management (**CIKM**)                    Apr 24 (1), 2020       Jul 03, 2020     Oct 19-23, 2020     Galway, Ireland                    17%                            `Link \u003chttps://cikm2020.org/\u003e`_\nIEEE International Conference on Data Mining (**ICDM**)                                            Jun 12, 2020           Aug 20, 2020     Nov 17-20, 2020     Sorrento, Italy                    19.8%                          `Link \u003chttp://icdm2020.bigke.org/\u003e`_\nACM SIGMOD/PODS Conference (**SIGMOD**)                                                            Jul 09, 2019           Oct 03, 2019     Jun 14-19, 2020     Portland, Oregon, USA              18%                            `Link \u003chttps://sigmod2020.org\u003e`_\nACM International Conference on Web Search and Data Mining (**WSDM**)                              **Aug 16, 2020**       Oct 16, 2019     Mar 08-12, 2021     Jerusalem, Israe                   16.3%                          `Link \u003chttp://www.wsdm-conference.org/2021/\u003e`_\n=================================================================================================  =====================  ===============  ==================  =================================  =============================  ===========================================================================================\n\n\n----\n\n\n2. Data Mining Conference Acceptance Rate\n-----------------------------------------\n\n\n===============================================  ============================================================================================  ==============================================================================\nConference                                       Acceptance Rate                                                                               Oral Presentation (otherwise poster)\n===============================================  ============================================================================================  ==============================================================================\nKDD '19                                          17.8% (321/1808)                                                                              N/A\nKDD '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)\nKDD '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)\nKDD '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)\nSDM '19                                          22.7% (90/397)                                                                                N/A\nSDM '18                                          23.0% (86/374)                                                                                N/A\nSDM '17                                          26.0% (93/358)                                                                                N/A\nSDM '16                                          26.0% (96/370)                                                                                N/A\nICDM '19*\\                                       18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper)                 N/A\nICDM '18*\\                                       19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper)                 N/A\nICDM '17*\\                                       19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper)                  N/A\nICDM '16*\\                                       19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper)                 N/A\nCIKM '19                                         19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research)  N/A\nCIKM '18                                         17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper)        Short papers are presented at poster sessions\nCIKM '17                                         20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper)                Short papers are presented at poster sessions\nCIKM '16                                         23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages)      Short papers are presented at poster sessions\nECML PKDD '18                                    26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track)     N/A\nECML PKDD '17                                    28% (104/364)                                                                                 N/A\nECML PKDD '16                                    28% (100/353)                                                                                 N/A\nPAKDD '19                                        24.1% (137/567, overall)                                                                      N/A\nPAKDD '18                                        27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular)          N/A\nPAKDD '17                                        28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular)           N/A\nPAKDD '16                                        29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular)           N/A\nWSDM '19                                         16.4% (84/511, overall)                                                                       40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^\\\nWSDM '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)^\\\nWSDM '17                                         15.8% (80/505)                                                                                30% (24/80, long presentation), 70% (56/80, short presentation)^\\\nWSDM '16                                         18.2% (67/368)                                                                                29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^\\\nWSDM '15                                         16.4% (39/238)                                                                                53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^\\\n===============================================  ============================================================================================  ==============================================================================\n\n*\\ 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.\nSee `ICDM Acceptance Rates \u003chttp://www.cs.uvm.edu/~icdm/ICDMAcceptanceRates.shtml\u003e`_ for more information.\n\n^\\ All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations.\n\nConference stats are visualized below for a straightforward comparison.\n\n.. image:: https://github.com/yzhao062/data-mining-conferences/blob/master/conference_stats.png\n   :target: https://github.com/yzhao062/data-mining-conferences/blob/master/conference_stats.png\n   :alt: Conference Stats\n\n----\n\n\n3. Conference Ranking\n---------------------\n\n\n=================================================================================================  =====================  ===============  ==================  =================================\nConference                                                                                         CORE (2018)            Qualis (2016)    CCF (2019)          ERA (2010)\n=================================================================================================  =====================  ===============  ==================  =================================\nACM SIGKDD International Conference on Knowledge discovery and data mining (**KDD**)               A*\\                    A1               A                   A\nEuropean Conference on Machine learning and knowledge discovery in databases (**ECML PKDD**)       A                      A1               B                   A\nIEEE International Conference on Data Mining (**ICDM**)                                            A*\\                    A1               B                   A\nSIAM International Conference on Data Mining (**SDM**)                                             A                      A1               B                   A\nACM International Conference on Information and Knowledge Management (**CIKM**)                    A                      A1               B                   A\nACM International Conference on Web Search and Data Mining (**WSDM**)                              A*\\                    A1               B                   B\nPacific-Asia Conference on Knowledge Discovery and Data Mining (**PAKDD**)                         A                      A2               C                   A\nThe Web Conference (**WWW**)                                                                       A*\\                    A1               A                   A\nIEEE International Conference on Data Engineering (**ICDE**)                                       A*\\                    A1               A                   A\n=================================================================================================  =====================  ===============  ==================  =================================\n\nSource and ranking explanation:\n\n* `CORE (2018) \u003chttp://portal.core.edu.au/conf-ranks/\u003e`_\n* `Qualis (2016) \u003chttps://www.capes.gov.br/images/documentos/Qualis_periodicos_2016/Qualis_conferencia_ccomp.pdf\u003e`_\n* `CCF (2019) \u003chttps://www.ccf.org.cn/xspj/sjk/sjwj/nrjs/\u003e`_\n* `ERA (2010) \u003chttp://www.conferenceranks.com/#data\u003e`_\n\n\n----\n\n\n4. Tips for Doing Good DM Research \u0026 Get it Published!\n------------------------------------------------------\n\n\n`How to do good research, Get it published in SIGKDD and get it cited! \u003chttp://www.cs.ucr.edu/~eamonn/Keogh_SIGKDD09_tutorial.pdf\u003e`_\\ :\na fantastic tutorial on SIGKDD'09 by Prof. Eamonn Keogh (UC Riverside).\n\n`Checklist for Revising a SIGKDD Data Mining Paper \u003chttps://web.cs.dal.ca/~eem/gradResources/KDD/Checklist%20for%20Revising%20a%20SIGKDD%20Data%20Mining%20Paper.pdf\u003e`_\\ :\na concise checklist by Prof. Eamonn Keogh (UC Riverside).\n\n`How to Write and Publish Research Papers for the Premier Forums in Knowledge \u0026 Data Engineering \u003chttp://acsic.org/files/Writing16-Web.pdf\u003e`_\\ :\na tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette).\n\n----\n\nReferences\n----------\n\n\n.. [#ibmresearch] IBM Research, 2018. Knowledge Discovery and Data Mining. https://researcher.watson.ibm.com/researcher/view_group.php?id=144\n\n\nLast updated @ May 12th, 2019","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyzhao062%2Fdata-mining-conferences","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyzhao062%2Fdata-mining-conferences","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyzhao062%2Fdata-mining-conferences/lists"}