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THE 19TH AUSTRALASIAN DATA MINING CONFERENCE 2021 (AUSDM’21)

Brisbane, Australia, 13-15 December 2021
The conference is planned to hold in-person in Brisbane. Participants from Australia and New Zealand are encouraged to attend it personally. There will be an option for overseas participants to attend it virtually.

Welcome to AusDM’21

  • Paper submissions: 24 August 7 Sept 2021
  • Notification: 30 September 2021
  • Camera-ready: 15 October 2021

The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.

Since AusDM’02 the conference has showcased research in data mining, providing a forum for presenting and discussing the latest research and developments. Built on this tradition, AusDM’21 will facilitate the cross-disciplinary exchange of ideas, experience and potential research directions. Specifically, the conference seeks to showcase: Research Prototypes; Industry Case Studies; Practical Analytics Technology; and Research Student Projects. AusDM’21 will be a meeting place for pushing forward the frontiers of data mining in academia and industry.


Publication and Topics


We are calling for papers, both research and applications, and from both academia and industry, for publication and presentation at the conference. All papers will go through double-blind, peer–review by a panel of international experts. The AusDM’21 proceeding will be published by Springer-Verlag and become available immediately after the conference.

Please note that AusDM’21 requires that at least one author for each accepted paper register for the conference and present their work.

AusDM invites contributions addressing current research in data mining and knowledge discovery as well as experiences, novel applications and future challenges.

Topics of interest include, but are not restricted to

• Applications and Case Studies — Lessons and Experiences
• Big Data Analytics
• Biomedical and Health Data Mining
• Business Analytics
• Computational Aspects of Data Mining
• Data Integration, Matching and Linkage
• Data Mining in Education
• Data Mining in Security and Surveillance
• Data Preparation, Cleaning and Preprocessing
• Data Stream Mining
• Deep Learning
• Evaluation of Results and their Communication
• Implementations of Data Mining in Industry
• Integrating Domain Knowledge
• Link, Tree, Graph, Network and Process Mining
• Machine Learning
• Multimedia Data Mining
• New Data Mining Algorithms
• Professional Challenges in Data Mining
• Privacy-preserving Data Mining
• Spatial and Temporal Data Mining
• Text Mining
• Visual Analytics
• Web and Social Network Mining


Keynote Speakers


Jeremy Howard, CSIRO, fast.ai

Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at platform.ai.

Previously, Jeremy was the founding CEO Enlitic, which was the first company to apply deep learning to medicine, and was selected as one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects.

He has many media appearances, including writing for the Guardian, USA Today, and the Washington Post, appearing on ABC (Good Morning America), MSNBC (Joy Reid), CNN, Fox News, BBC, and was a regular guest on Australia’s highest-rated breakfast news program. His talk on TED.com, “The wonderful and terrifying implications of computers that can learn”, has over 2.5 million views. He is a co-founder of the global Masks4All movement.

  • More to come

Submission of Papers


We invite three types of submissions for AusDM’21:

  • Research Track: Academic submissions reporting on new algorithms, novel approaches and research progress, with a paper length of between 8 and 15 pages in Springer CCIS style, as detailed below.
  • Application Track: Submissions reporting on applications of data mining and machine learning and describing specific data mining implementations and experiences in the real world. Submissions in this category can be between 6 and 15 pages in Springer CCIS style, as detailed below.
  • Industry Showcase Track: Submissions from governments and industry on an analytics solution that has raised profits, reduced costs and/or achieved other important policy and/or business outcomes can be made in this track. Submissions to this category should be a 1-page extended abstract. Note that this track is presentation only, without publication in conference proceedings. For publication of your papers, please submit them to the above Application Track.

All submissions, except for the Industry Showcase Track, will go through a double-blind review process, i.e. paper submissions must NOT include authors names or affiliations or acknowledgments referring to funding bodies. Self-citing references should also be removed from the submitted papers for the double-blinded reviewing purpose. The information can be added in the accepted final camera-ready submissions.

All submissions are required to follow the format specified for papers in the Springer Communications in Computer and Information Science (CCIS) style. Authors should consult Springer’s authors’ guidelines and use the proceeding templates, either in LaTeX or Word, for the preparation of their papers. The electronic submission must be in PDF only and made through the AusDM’21 Submission page. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each paper, acting on behalf of all the authors of the paper, must complete and sign a Consent to Publish form, through which the copyright for their paper is transferred to Springer.

If the Springer’s authors’ guidelines or proceeding templates (LaTeX or Word) links are not opening on your browser, you can get them from here.

Submitted papers must not be previously published or accepted for publication anywhere. They must not be submitted to any other conference or journal during the review process of AusDM’21.

Submit your paper through EasyChair at here.

Important Dates


  • Paper submissions: 24 August 7 Sept 2021
  • Notification: 30 September 2021
  • Camera-ready: 15 October 2021
  • Conference: 13-15 December 2021

Organising Committee


Conference Chairs
• Richi Nayak, Queensland University of Technology
• Yanchang Zhao, Data61, CSIRO
• Graham William, The Australian National University

Program Chairs
• Yue Xu, Queensland University of Technology
• Rosalind Wang, Western Sydney University
• Anton Lord, Leap In!

Organising Chairs
• Khanh Luong, Queensland University of Technology
• Thirunavukarasu Balasubramaniam, Queensland University of Technology

Publicity Chair
• Md Abul Bashar, Queensland University of Technology

Publication Chair
• Yee Ling Boo, RMIT University

Industry Track Program Chairs
• Annette Slunjski, IAPA
• Warwick Graco, ATO

Steering Committee
• Simeon Simoff (Chair), University of Western Sydney
• Graham William (Chair), The Australian National University
• Peter Christen, The Australian National University
• Ling Chen, University of Technology
• Zahid Islam, Charles Sturt University
• Paul Kennedy, University of Technology
• Yun Sing Koh, The University of Auckland
• Jiuyong (John) Li, University of South Australia
• Richi Nayak, Queensland University of Technology
• Kok–Leong Ong, La Trobe University
• Dharmendra Sharma, University of Canberra
• Glenn Stone, Western Sydney University
• Yanchang Zhao, Data61, CSIRO


Further Information


AusDM’21 website: https://ausdm21.ausdm.org/
Contact the organisers of AusDM 2020 at ausdm21@ausdm.org
AusDM LinkedIn Group: https://www.linkedin.com/groups/4907891/

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