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KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM2013 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Chicago Illinois USA August 11 - 14, 2013
ISBN:
978-1-4503-2174-7
Published:
11 August 2013
Sponsors:

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Abstract

It is our great pleasure to welcome you to the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). The annual ACM SIGKDD conference is the premier international forum for data mining, knowledge discovery and big data. It brings together researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2013 features plenary presentations, paper presentations, poster sessions, workshops, tutorials, exhibits, demonstrations, and the KDD Cup competition.

Today, you hear a lot about big data, data science and data intensive computing. The core of this work is extracting knowledge and useful information from data, which for science leads to beautiful insights, and for applications leads to actions, alerts and decisions. The KDD community has always been at the center of this activity and it is clear from this conference that it will continue to drive this broader field of big data.

This year there were 726 submissions to the KDD Research Track, and 125 papers were accepted. There were 136 submissions to the KDD Industry and Government Track, and 34 papers were accepted.

KDD also has a history of inviting talks that are of broad interest to the KDD community. This year we chose to have 4 plenary talks. A program committee also selected 8 talks to present at the Industry Practice Exposition.

A strength of the KDD conference is the number of workshops and tutorials that are co-located with it. This year there were 10 full-day workshops, 5 half-day workshops, and 6 tutorials.

We thank all sponsors, who are a very important part of the conference, and the members of the Organizing Committee and our other colleagues who volunteered their time during the past year to make this conference a success. Special thanks goes to the Research Track Co-Chairs and the Industry and Government Track Co-Chairs. Also special thanks are due to the Local Arrangements Chair, the Treasurer, the Proceedings Co-Chairs, and the KDD Cup Committee.

We are grateful to the several program committees that provided the advice necessary to put together a quality program - the Research Track Program Committee, the Research Track Senior Program Committee, the Industry and Government Track Program Committee, the Industry Practice Expo Program Committee, the Workshop Program Committee, the Tutorial Program Committee, and the Demo Program Committee.

We know that you will find this year's exhibits and demonstrations exciting and remind you that some of the most interesting discussions can be found there.

Please join us for KDD-2013 to gain new knowledge and to exchange exciting new research results, leading practices, and high impact applications in big data, knowledge discovery and data mining. We hope that you will find this program interesting and thought-provoking and that the conference will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world.

Cited By

  1. Drożdż A, Duggan B, Ruddock M, Reid C, Kurth M, Watt J, Irvine A, Lamont J, Fitzgerald P, O’Rourke D, Curry D, Evans M, Boyd R and Sousa J (2024). Stratifying risk of disease in haematuria patients using machine learning techniques to improve diagnostics, Frontiers in Oncology, 10.3389/fonc.2024.1401071, 14
  2. Li X, Sun L, Chen S, Wang H, Wen Q, Zhang X, Ding X and Loskot P (2023). Behavior sequence aggregation and attention mechanism-based interest recommendation Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 10.1117/12.3005141, 9781510668355, (130)
  3. Udin M, Ionita C, Pokharel S, Sharma U, Iftekharuddin K, Drukker K, Mazurowski M, Lu H, Muramatsu C and Samala R (2022). Automation of ischemic myocardial scar detection in cardiac magnetic resonance imaging of the left ventricle using machine learning Computer-Aided Diagnosis, 10.1117/12.2612234, 9781510649415, (67)
  4. Su Y, Kong X, Liu G and Su J (2021). Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model, Security and Communication Networks, 2021, Online publication date: 1-Jan-2021.
  5. Xiao Y, Lu X and Liu Y (2016). A parallel and distributed algorithm for role discovery in large-scale social networks, Intelligent Automation & Soft Computing, 10.1080/10798587.2016.1152777, 22:4, (675-681), Online publication date: 1-Oct-2016.
  6. Kolodny M, Damarla T, Chatters G, Liss B, Vu H and Sabatier J (2014). An algorithm for monitoring the traffic on a less-travelled road using multi-modal sensor suite SPIE Defense + Security, 10.1117/12.2050318, , (90790F), Online publication date: 10-Jun-2014.
  7. Whinston A and Rui H Social Media as an Innovation - The Case of Twitter, SSRN Electronic Journal, 10.2139/ssrn.1564205
Contributors
  • Carnegie Mellon University
  • IBM Thomas J. Watson Research Center
  • The University of Chicago
  • General Motors
  • Google LLC, Africa & Middle East

Index Terms

  1. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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          Recommendations

          Acceptance Rates

          KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;
          Overall Acceptance Rate 1,133 of 8,635 submissions, 13%
          YearSubmittedAcceptedRate
          KDD '191,2001109%
          KDD '1898310711%
          KDD '17748649%
          KDD '161,115666%
          KDD '1581916020%
          KDD '141,03615115%
          KDD '1372612517%
          KDD '0859311820%
          KDD '0757311119%
          KDD '032984615%
          KDD '023074414%
          KDD '012373113%
          Overall8,6351,13313%