The Conferences

The conference web sites and proceedings are available as below.

• AusDM23 : 11-13 December 2023, Auckland, New Zealand (Alternative)
• AusDM22 : 12-16 December 2022, Western Sydney (Alternative)
• AusDM21 : 12-15 December 2021, Brisbane, Qld (Alternative)
• AusDM20 : 1-4 December 2020, Canberra, ACT (IEEE Conference Co-host) (Alternative)
• AusDM19 : 2-5 December 2019, Adelaide, SA (Springer)
• AusDM18 : 28-30 November 2018, Bathurst, NSW (Springer)
• AusDM17 : 19-20 August 2017, Melbourne (IJCAI co-host) (Springer)
• AusDM16 : 4-9 December 2016, Realm Hotel, Canberra (ASC co-host) (Proceedings)
• AusDM15 : 8-9 August 2015, Sydney (KDD co-host) (Proceedings)
• AusDM14 : 27-28 November 2014, QUT, Brisbane (Proceedings)
• AusDM13 : 13-15 November 2013, Canberra (Proceedings)
• AusDM12 : 5-7 December 2012, Sydney (Proceedings)
• AusDM11 : 1-2 December 2011, Ballarat (Proceedings)
• AusDM09 : 1-4 December 2009, Melbourne (Proceedings)
• AusDM08 : 27-28 November 2008, Adelaide (Proceedings)
• AusDM07 : 3-4 December 2007, Gold Coast (Proceedings)
• AusDM06 : 29-30 November 2006, Sydney (Proceedings)
• AusDM05 : 5-7 December 2005, Sydney (Proceedings)
• AusDM04 : 6 December 2004, Cairns (Proceedings)
• AusDM03 : 8 December 2003, Canberra (Proceedings)
• AusDM02 : 3 December 2002, Canberra (Proceedings)

Topics of interest

The major topics of the conference have included

• Applications using data science and machine learning
• Deep learning algorithms
• Knowledge graph and data representation
• Implementations of machine learning algorithms
• Data science methods and algorithms
• Integrating data sets
• High performance computing and machine learning
• Infrastructure for machine learning and data science
• Health data
• Finance data
• Spatial and temporal data
• Multimedia and web data
• e-Business environments and virtual communities
• Networks
• Case studies and their lessons and experiences
• Bio-informatics
• Visualisation techniques
• Integrating domain knowledge
• Embedded machine learning technologies
• Research and professional challenges
• Qualitative research
• Data cleaning and data linking

Further information from the AusDM Steering Committee via sc@ausdm.org.