AusDM: Australasian Data Mining Conference



Past:   Proceedings on DBLP

Future:  Post a CFP for 2024 or later   |   Invite the Organizers Email


All CFPs on WikiCFP

Event When Where Deadline
AUSDM 2023 The Australasian Data Science and Machine Learning Conference 2023 - First Call for Papers
Dec 11, 2023 - Dec 13, 2023 Auckland, New Zealand Aug 25, 2023 (Aug 18, 2023)
Dec 12, 2022 - Dec 16, 2022 Western Sydney Aug 12, 2022 (Aug 5, 2022)
AusDM 2021 The 19th Australasian Data Mining Conference 2021
Dec 13, 2021 - Dec 15, 2021 Brisbane, Australia Sep 7, 2021
AusDM 2020 The 18th Australasian Data Mining Conference 2020
Dec 1, 2020 - Dec 4, 2020 Canberra, Australia Aug 7, 2020
AusDM 2019 The 17th Australasian Data Mining Conference
Dec 2, 2019 - Dec 5, 2019 Adelaide, Australia Aug 12, 2019
AusDM 2018 Australasian Data Mining Conference
Nov 28, 2018 - Nov 30, 2018 Bathurst, Australia Jul 20, 2018
AusDM 2017 15th Australasian Data Mining Conference
Aug 19, 2017 - Aug 25, 2017 Melbourne, Australia May 31, 2017
AusDM 2013 The 11th Australasian Data Mining Conference
Nov 13, 2013 - Nov 15, 2013 Canberra, Australia Jul 15, 2013
AusDM 2012 The 10th Australasian Data Mining Conference
Dec 5, 2012 - Dec 7, 2012 Sydney, Australia Aug 1, 2012
AusDM 2011 Ninth Australasian Data Mining Conference
Dec 1, 2011 - Dec 2, 2011 Ballarat, Victoria, Australia Aug 22, 2011 (Jun 24, 2011)
AusDM 2009 The Australasian Data Mining Conference
Dec 1, 2009 - Dec 4, 2009 Melbourne, Australia Jul 31, 2009
AusDM 2008 The Australasian Data Mining Conference
Nov 27, 2008 - Nov 28, 2008 Adelaide, Australia Aug 1, 2008

Present CFP : 2023

AUSDM 2023 - Call for Papers *Extended Deadline*

Auckland, New Zealand, 11 - 13 December 2023

Call for Papers
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.

Keydates (Timezone: AoE)
Abstract submission: 18 Aug 23
Paper submission: 25 Aug 23
Paper notification: 24 Sept 23
Camera-ready: 8 Oct 23
Author Registration: 8 Oct 23
Conference: 11-13 Dec 23

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.

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.

Special Sessions
To celebrate the first time of AusDM in New Zealand, this year we have lined-up a set of special topics to facilitate sharing and learning of the latest research advances in data science and machine learning areas of high relevance for today’s society. These sessions are chaired by recognised experts in the field. We strongly encourage submissions in these areas (please see research-track submission guidelines here):

Ethics and Society (Chair: Dr Amanda J. Williamson, Generative AI Lead, Deloitte New Zealand / Senior Lecturer, University of Waikato): Societal implications of data science and machine learning, fairness, interpretability, transparency, trustworthiness, human-in-the-loop machine learning, climate change and sustainability.
Generative modelling (Chair: Dr Jiamou Liu, Senior Lecturer, The University of Auckland): including multimodal ML, ML for coding, ML for drug discovery / molecular tasks, and generative models in general.

Interdisciplinary applications of ML/data science (Chair: Dr Mingming Gong, Senior Lecturer, The University of Melbourne): applications in social sciences, urban planning, medicine, humanities, social good, etc.

Organizing Committee
Steering Committee Chairs
Simeon Simoff (Western Sydney University)
Graham Williams (The Australian National University)

General Chairs
Yun Sing Koh (University of Auckland)
Annette Slunjski (IAPA)

PC Chairs – Research
Diana Benavides Prado (University of Auckland)
Sarah Monazam Erfani (University of Melbourne)

PC Chairs – Application
Philippe Fournier-Viger (Shenzhen University)

Tutorial and Workshop Chair
Andrew Lensen (Victoria University of Wellington)

Publication Chair
Yee Ling Boo (RMIT University)

Sponsorship Chair
Annelies Tjetjep (Data & Analytics, PwC)

Doctoral Symposium Chair
Di Zhao (University of Auckland)
Asara Senaratne (The Australian National University)

Diversity, Equity, and Inclusion Chair
Richi Nayak (Queensland University of Technology)

Web Chair
Bowen Chen (University of Auckland)

Publicity Chair
Nick Lim (University of Waikato)


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