├── conference_stats.png ├── LICENSE ├── plot.py └── README.rst /conference_stats.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yzhao062/data-mining-conferences/HEAD/conference_stats.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Yue Zhao 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /plot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """A plotting utility for conference stats 3 | """ 4 | # Author: Yue Zhao 5 | # License: BSD 2 clause 6 | 7 | 8 | import numpy as np 9 | import matplotlib.pyplot as plt 10 | 11 | import matplotlib 12 | 13 | # rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) 14 | matplotlib.rcParams['mathtext.fontset'] = 'cm' 15 | matplotlib.rcParams['font.family'] = 'STIXGeneral' 16 | 17 | n_groups = 7 18 | 19 | submitted_2016 = np.asarray([784, 370, 904, 701, 353, 368, 307]) 20 | accepted_2016 = np.asarray([142, 96, 178, 160, 100, 67, 91]) 21 | rejected_2016 = submitted_2016 - accepted_2016 22 | accp_rate_2016 = np.rint((accepted_2016 / submitted_2016) * 100).astype(int) 23 | 24 | submitted_2017 = np.asarray([748, 358, 778, 855, 364, 505, 458]) 25 | accepted_2017 = np.asarray([130, 93, 155, 171, 104, 80, 129]) 26 | rejected_2017 = submitted_2017 - accepted_2017 27 | accp_rate_2017 = np.rint((accepted_2017 / submitted_2017) * 100).astype(int) 28 | 29 | submitted_2018 = np.asarray([983, 374, 948, 826, 354, 514, 592]) 30 | accepted_2018 = np.asarray([181, 86, 188, 147, 94, 84, 164]) 31 | rejected_2018 = submitted_2018 - accepted_2018 32 | accp_rate_2018 = np.rint((accepted_2018 / submitted_2018) * 100).astype(int) 33 | 34 | fig, ax = plt.subplots() 35 | 36 | fig.set_size_inches(12, 8) 37 | 38 | index = np.arange(n_groups) 39 | bar_width = 0.28 40 | 41 | opacity = 0.4 42 | error_config = {'ecolor': '0.3'} 43 | 44 | rects2016_rejected = ax.bar(index, 45 | rejected_2016, 46 | bar_width, 47 | alpha=opacity, 48 | color='darkred', 49 | error_kw=error_config, 50 | label='2016_rejected') 51 | 52 | rects2016_accepted = ax.bar(index, 53 | accepted_2016, 54 | bar_width, 55 | bottom=rejected_2016, 56 | alpha=opacity, 57 | color='red', 58 | error_kw=error_config, 59 | label='2016_accepted') 60 | 61 | rects2016 = ax.bar(index, 62 | submitted_2016, 63 | bar_width, 64 | alpha=0.2, 65 | color='white', 66 | error_kw=error_config, 67 | # label='2016' 68 | ) 69 | 70 | rects2017_rejected = ax.bar(index + bar_width, 71 | rejected_2017, 72 | bar_width, 73 | alpha=opacity, 74 | color='darkgreen', 75 | error_kw=error_config, 76 | label='2017_rejected') 77 | 78 | rects2017_accepted = ax.bar(index + bar_width, 79 | accepted_2017, 80 | bar_width, 81 | bottom=rejected_2017, 82 | alpha=opacity, 83 | color='mediumseagreen', 84 | error_kw=error_config, 85 | label='2017_accepted') 86 | 87 | rects2017 = ax.bar(index + bar_width, 88 | submitted_2017, 89 | bar_width, 90 | alpha=0.2, 91 | color='white', 92 | error_kw=error_config, 93 | # label='2017' 94 | ) 95 | 96 | rects2018_rejected = ax.bar(index + bar_width * 2, 97 | rejected_2018, 98 | bar_width, 99 | alpha=opacity, 100 | color='darkblue', 101 | error_kw=error_config, 102 | label='2018_rejected') 103 | 104 | rects2018_accepted = ax.bar(index + bar_width * 2, 105 | accepted_2018, 106 | bar_width, 107 | bottom=rejected_2018, 108 | alpha=opacity, 109 | color='slateblue', 110 | error_kw=error_config, 111 | label='2018_accepted') 112 | 113 | rects2018 = ax.bar(index + bar_width * 2, 114 | submitted_2018, 115 | bar_width, 116 | alpha=0.2, 117 | color='white', 118 | error_kw=error_config, 119 | # label='2018' 120 | ) 121 | 122 | ax.set_xlabel('Major Data Minining Conferences', fontsize=15) 123 | ax.set_ylabel('# Papers & Acceptance Rate', fontsize=15) 124 | ax.set_title('Conference Statistics (2016-2018)', fontsize=18) 125 | ax.set_xticks(index + bar_width) 126 | ax.set_xticklabels(('KDD', 'SDM', 'ICDM', 'CIKM', 'PKDD', 'WSDM', 'PAKDD')) 127 | ax.legend() 128 | 129 | 130 | def autolabel(rects, accp_rates, accps, rejs): 131 | """ 132 | Attach a text label above each bar displaying relevant information 133 | """ 134 | for rect, accp_rate, accp, rej in zip(rects, accp_rates, accps, rejs): 135 | height = rect.get_height() 136 | ax.text(rect.get_x() + rect.get_width() / 2., height + 10, 137 | str(accp_rate) + '%', 138 | ha='center', va='bottom') 139 | ax.text(rect.get_x() + rect.get_width() / 2., 0.9 * height, 140 | '%d' % int(accp), 141 | ha='center', va='bottom') 142 | ax.text(rect.get_x() + rect.get_width() / 2., 0.4 * height, 143 | '%d' % int(rej), 144 | ha='center', va='bottom') 145 | 146 | 147 | autolabel(rects2016, accp_rate_2016, accepted_2016, rejected_2016) 148 | autolabel(rects2017, accp_rate_2017, accepted_2017, rejected_2017) 149 | autolabel(rects2018, accp_rate_2018, accepted_2018, rejected_2018) 150 | plt.savefig('conference_stats.png', dpi=330) 151 | plt.show() 152 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | Data Mining Conferences 2 | ======================= 3 | 4 | ---- 5 | 6 | **Knowledge Discovery and Data Mining** is an interdisciplinary area focusing 7 | upon methodologies and applications for extracting useful knowledge from data [#ibmresearch]_. 8 | Different from machine learning, Knowledge Discovery and Data Mining (KDD) is 9 | considered to be more practical and more related with real-world applications. 10 | Some good examples include recommender systems, clustering, graph mining, 11 | anomaly detection, and ensemble learning. 12 | 13 | To facilitate KDD related research, we create this repository with: 14 | 15 | * **Upcoming data mining (DM) conference** submission date, notification date, and etc. 16 | * **Historical conference acceptance rate** 17 | * **Conference ranking** by `CORE (2018) `_, `Qualis (2016) `_, `CCF (2015) `_, and ERA (2012) 18 | * **Publication tips** from field experts 19 | 20 | 21 | **Table of Contents**\ : 22 | 23 | * `1. 2020-2021 Data Mining Conferences`_ 24 | * `2. Data Mining Conference Acceptance Rate`_ 25 | * `3. Conference Ranking`_ 26 | * `4. Tips for Doing Good DM Research & Get it Published!`_ 27 | 28 | 29 | ---- 30 | 31 | 32 | 1. 2020-2021 Data Mining Conferences 33 | ------------------------------------ 34 | 35 | 36 | ================================================================================================= ===================== =============== ================== ================================= ============================= =========================================================================================== 37 | Conference Submission Deadline Notification Conference Date Location Acceptance Rate (2018) Website 38 | ================================================================================================= ===================== =============== ================== ================================= ============================= =========================================================================================== 39 | IEEE International Conference on Big Data (**BigData**) **Aug 26, 2020** Oct 20, 2020 Dec 10-13, 2020 Virtual 19.7% `Link `_ 40 | AAAI Conference on Artificial Intelligence (**AAAI**) **Sep 01 (09), 2020** Dec 01, 2020 Feb 02-09, 2021 Virtual 20.6% `Link `_ 41 | 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 `_ 42 | SIAM International Conference on Data Mining (**SDM**) **Sep 21, 2020** Dec TBA, 2020 Mar 25-27, 2021 Alexandria, Virginia, USA 22.9% `Link `_ 43 | The Web Conference (**WWW**) **Oct 12 (19), 2020** Jan 15, 2021 Apr 19-23, 2021 Ljubljana 15% `Link `_ 44 | IEEE International Conference on Data Engineering (**ICDE**) Oct 08 (15), 2019 Dec 14, 2019 Apr 20-24, 2020 Dallas, Texas, USA 18% `Link `_ 45 | 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 `_ 46 | 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 `_ 47 | 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 `_ 48 | ACM International Conference on Information and Knowledge Management (**CIKM**) Apr 24 (1), 2020 Jul 03, 2020 Oct 19-23, 2020 Galway, Ireland 17% `Link `_ 49 | IEEE International Conference on Data Mining (**ICDM**) Jun 12, 2020 Aug 20, 2020 Nov 17-20, 2020 Sorrento, Italy 19.8% `Link `_ 50 | ACM SIGMOD/PODS Conference (**SIGMOD**) Jul 09, 2019 Oct 03, 2019 Jun 14-19, 2020 Portland, Oregon, USA 18% `Link `_ 51 | 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 `_ 52 | ================================================================================================= ===================== =============== ================== ================================= ============================= =========================================================================================== 53 | 54 | 55 | ---- 56 | 57 | 58 | 2. Data Mining Conference Acceptance Rate 59 | ----------------------------------------- 60 | 61 | 62 | =============================================== ============================================================================================ ============================================================================== 63 | Conference Acceptance Rate Oral Presentation (otherwise poster) 64 | =============================================== ============================================================================================ ============================================================================== 65 | KDD '19 17.8% (321/1808) N/A 66 | 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) 67 | 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) 68 | 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) 69 | SDM '19 22.7% (90/397) N/A 70 | SDM '18 23.0% (86/374) N/A 71 | SDM '17 26.0% (93/358) N/A 72 | SDM '16 26.0% (96/370) N/A 73 | ICDM '19*\ 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper) N/A 74 | ICDM '18*\ 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper) N/A 75 | ICDM '17*\ 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper) N/A 76 | ICDM '16*\ 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper) N/A 77 | CIKM '19 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research) N/A 78 | CIKM '18 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper) Short papers are presented at poster sessions 79 | CIKM '17 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper) Short papers are presented at poster sessions 80 | 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 81 | ECML PKDD '18 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track) N/A 82 | ECML PKDD '17 28% (104/364) N/A 83 | ECML PKDD '16 28% (100/353) N/A 84 | PAKDD '19 24.1% (137/567, overall) N/A 85 | PAKDD '18 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular) N/A 86 | PAKDD '17 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular) N/A 87 | PAKDD '16 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular) N/A 88 | WSDM '19 16.4% (84/511, overall) 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^\ 89 | 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)^\ 90 | WSDM '17 15.8% (80/505) 30% (24/80, long presentation), 70% (56/80, short presentation)^\ 91 | WSDM '16 18.2% (67/368) 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^\ 92 | WSDM '15 16.4% (39/238) 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^\ 93 | =============================================== ============================================================================================ ============================================================================== 94 | 95 | *\ 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. 96 | See `ICDM Acceptance Rates `_ for more information. 97 | 98 | ^\ All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations. 99 | 100 | Conference stats are visualized below for a straightforward comparison. 101 | 102 | .. image:: https://github.com/yzhao062/data-mining-conferences/blob/master/conference_stats.png 103 | :target: https://github.com/yzhao062/data-mining-conferences/blob/master/conference_stats.png 104 | :alt: Conference Stats 105 | 106 | ---- 107 | 108 | 109 | 3. Conference Ranking 110 | --------------------- 111 | 112 | 113 | ================================================================================================= ===================== =============== ================== ================================= 114 | Conference CORE (2018) Qualis (2016) CCF (2019) ERA (2010) 115 | ================================================================================================= ===================== =============== ================== ================================= 116 | ACM SIGKDD International Conference on Knowledge discovery and data mining (**KDD**) A*\ A1 A A 117 | European Conference on Machine learning and knowledge discovery in databases (**ECML PKDD**) A A1 B A 118 | IEEE International Conference on Data Mining (**ICDM**) A*\ A1 B A 119 | SIAM International Conference on Data Mining (**SDM**) A A1 B A 120 | ACM International Conference on Information and Knowledge Management (**CIKM**) A A1 B A 121 | ACM International Conference on Web Search and Data Mining (**WSDM**) A*\ A1 B B 122 | Pacific-Asia Conference on Knowledge Discovery and Data Mining (**PAKDD**) A A2 C A 123 | The Web Conference (**WWW**) A*\ A1 A A 124 | IEEE International Conference on Data Engineering (**ICDE**) A*\ A1 A A 125 | ================================================================================================= ===================== =============== ================== ================================= 126 | 127 | Source and ranking explanation: 128 | 129 | * `CORE (2018) `_ 130 | * `Qualis (2016) `_ 131 | * `CCF (2019) `_ 132 | * `ERA (2010) `_ 133 | 134 | 135 | ---- 136 | 137 | 138 | 4. Tips for Doing Good DM Research & Get it Published! 139 | ------------------------------------------------------ 140 | 141 | 142 | `How to do good research, Get it published in SIGKDD and get it cited! `_\ : 143 | a fantastic tutorial on SIGKDD'09 by Prof. Eamonn Keogh (UC Riverside). 144 | 145 | `Checklist for Revising a SIGKDD Data Mining Paper `_\ : 146 | a concise checklist by Prof. Eamonn Keogh (UC Riverside). 147 | 148 | `How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering `_\ : 149 | a tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette). 150 | 151 | ---- 152 | 153 | References 154 | ---------- 155 | 156 | 157 | .. [#ibmresearch] IBM Research, 2018. Knowledge Discovery and Data Mining. https://researcher.watson.ibm.com/researcher/view_group.php?id=144 158 | 159 | 160 | Last updated @ May 12th, 2019 --------------------------------------------------------------------------------