The Australasian Data Science and Machine Learning Conference (AusDM), formerly known as 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 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’23 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’23 will be a meeting place for pushing forward the frontiers of data science and machine learning in academia and industry.

AusDM’23 will deliver keynote speeches, invited talks, full paper presentations, abstracts, tutorials, workshops, social events, etc.

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 2023 proceedings will be published by Springer Communications in Computer and Information Science (CCIS) and become available immediately after the conference. Please note that AusDM’23 requires that at least one author for each accepted paper register for the conference and present their work for the paper to be published in the proceeding.

AusDM’23 invites contributions addressing current research in data mining and knowledge discovery as well as experiences, novel applications and future challenges.

General topics of interest include, but are not restricted to:

• • Big Data Analytics
  • Biomedical and Health Data Mining
  • Computational Aspects of Data Mining
  • Data Integration, Matching and Linkage
  • Data Mining in Security and Surveillance
  • Data Preparation, Cleaning and Preprocessing
  • Data Stream Mining
  • Deep Learning
  • Machine Learning Safety
  • Evaluation of Results and their Communication

AusDM’23 specially invites contributions for the following special topics:

• • Ethics and Society: Societal implications of data science and machine learning, fairness, interpretability, transparency, trustworthiness, human-in-the-loop machine learning, climate change and sustainability
  • Generative modelling: multimodal ML, ML for coding, ML for drug discovery / molecular tasks, 
  • Interdisciplinary applications of ML/data science: in social sciences, urban planning, medicine, humanities, social good, etc.

Keynote Speakers

As is tradition for AusDM we have lined up an excellent keynote speaker program. Each speaker is a well-known researcher and/or practitioner in data mining and related disciplines. The keynote program provides an opportunity to hear from some of the world’s leaders on what the technology offers and where it is heading.

Submissions

We invite three types of submissions for AusDM’23:

• 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 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.

All 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’23. We would like to draw the authors’ attention to Springer’s Editorial Policies and Code of Conduct (available as a PDF here).

The link to previous AusDM proceedings on SpringerLink is available here.

A selected number of best papers will be invited for possible inclusion, in an expanded and revised form, in the Data Science and Engineering, Scimago Q1 journal published by Springer.