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KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ACM2015 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Sydney NSW Australia August 10 - 13, 2015
ISBN:
978-1-4503-3664-2
Published:
10 August 2015
Sponsors:
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Abstract

It is our great pleasure to welcome you to the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). Theannual ACM SIGKDD conference is the premier international forum for data science, 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-2015 features 4 plenary keynote presentations, 12 invited talks, 228 paper presentations, a discussion panel, a poster session, 14 workshops, 12 tutorials, 27 exhibition booths, the KDD Cup competition, and a banquet at the Dockside Pavilion at the Sydney Darling Harbour. As always, KDD-2015 attracted presenters and delegates from around the world. It is with great pleasure that we bring this international conference for the first time to the southern hemisphere.

This year we again had a strong set of submissions. There were 819 submissions to the Research Track, of which 160 papers were accepted. There were 189 submissions to the Industry & Government Track, of which 68 papers were accepted. All papers submitted to the Research Track and to the Industry & Government Tracks were subjected to a rigorous review process. They were initially screened by the Chairs of the respective tracks, and a small number of papers that did not comply with the formatting requirements or which violated the dual submission policy were summarily rejected. At least three reviewers and a metareviewer were assigned to all remaining papers based on the results of a bidding process. The authors were able to read the reviews and provide a response. The meta-reviewers then had an opportunity to consider all reviews and author responses. This then initiated a discussion during which all reviewers of a paper had the opportunity to read each other's reviews and the author responses and to update their reviews as appropriate. In a few cases, the meta-reviewers added another reviewer at this stage to gain expert opinion on specific issues. The meta-reviewers then made recommendations on acceptance or rejection to the track chairs. The track chairs then assessed the meta-reviews, reviews, author responses and discussions to make a final decision. In a few cases, they also solicited further expert reviews and meta-reviews to resolve specific questions. Thus, all papers were assessed by at least four and up to seven discipline experts. All accepted papers were presented both as a 20-minute talk and as a poster.

The Industry & Government Invited Talk Track features 12 talks from world renowned experts who have played a significant role in developing and deploying large-scale data mining applications and systems in their respective fields with clearly measurable and meaningful impact. We trust that this opportunity for the KDD community to hear directly from senior leaders in industry and government will inspire new advances and broader interdisciplinary collaboration between researchers, industry and government counterparts. Featured topics in this year's Industry & Government Invited Talk Track include applications in cloud intelligence, personal finance, insurance, fraud, marketing, advertising, and telecommunications. Additionally, we have focused on key enabling BigData infrastructure for KDD technologies with talks on highly-scalable and open source solutions that are quickly becoming mainstream.

We continued the conference's tradition of strong workshop and tutorial programs. This year there are 9 full-day workshops, 5 half-day workshops, and 12 tutorials.

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  3. Zhang Z, Ren L, You Y, Zhang K, Huang J and Zhu L (2022). Dense subgraph mining kmax-truss optimization algorithm 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 10.1117/12.2641426, 9781510657717, (54)
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  6. Hu H and Lee M (2022). Graph Neural Network-based Clustering Enhancement in VANET for Cooperative Driving 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 10.1109/ICAIIC54071.2022.9722625, 978-1-6654-5818-4, (162-167)
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  11. Ben-Assuli O and Padman R (2018). Analysing repeated hospital readmissions using data mining techniques, Health Systems, 10.1080/20476965.2018.1510040, 7:3, (166-180), Online publication date: 2-Sep-2018.
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Contributors
  • University of Technology Sydney
  • Cornell University
  • Monash University
  • Boeing Corporation
  • Australian Taxation Office, Canberra

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  1. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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          Acceptance Rates

          KDD '15 Paper Acceptance Rate160of819submissions,20%Overall Acceptance Rate1,133of8,635submissions,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%