Design a site like this with
Get started

Welcome to AusDM’20

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’20 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’20 will be a meeting place for pushing forward the frontiers of data mining in academia and industry. In this year, AusDM is pleased to be co-located with the 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2020) in Canberra, Australia.

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 Education
  • Data Mining in Security and Surveillance
  • Data Preparation, Cleaning and Preprocessing
  • Data Stream Mining
  • Evaluation of Results and their Communication
  • Implementations of Data Mining in Industry
  • Integrating Domain Knowledge
  • Link, Tree, Graph, Network and Process Mining
  • 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

AusDM’20 will feature three types of papers:

  • Research Track: Submissions reporting on new algorithms, novel approaches, research progress of data mining and machine learning.
  • Application Track: Submissions on specific data mining implementations and experiences in government and industry settings, applications of data mining and machine learning in the real world.

Submissions in these two categories will be reviewed using the IEEE SSCI paper management system, with accepted papers appearing in the IEEE SSCI 2020 proceedings. Instructions for authors are the same as other IEEE SSCI 2020 papers.

  • Industry Showcase Track: Submissions from governments and industry on innovative application of data mining and analytics solutions that have improved the quality of data products and have raised profits, reduced costs and/or achieved other important policy and/or business outcomes can be made in this track. Participants can either submit an extended abstract to be included in the conference program or a full paper to submit in the application track for peer-reviewed publication. Extended abstract can be submitted to directly and will be handled independently and will appear on the AusDM2020 website.  

Important Dates
Submissions: 7 August 2020 22 August 2020
Notification: 18 September 2020
Camera-ready: 2 October 2020
Early Registration Deadline: 2 October 2020
Conference: 1-4 December 2020


AusDM 2020 takes the safety and health of its attendees to heart.

The conference will be running as a virtual event.

The registration cost will be revised. The organisers are working on the revision of the logistics and aim to announce the revised registration by early August.

We will keep you informed.

Be safe, Be healthy, Be happy, and Be ready to submit your best work to AusDM 2020 on time.