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 two types of papers:

    Theoretical and Applied Research Track: Submissions from academia and practitioners reporting on new algorithms, novel approaches, research progress, applications of data mining and machine learning in the real world. Submissions in this category 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 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 with a paper length between 4 and 6 pages IEEE style. Papers from this track will be handled independently and will appear in a booklet to be handed out to participants.