Call for papers

We are calling for papers, both research and applications, and from both academia and industry, for presentation at the conference. All papers will go through double–blind, peer–review by a panel of international experts. Since 2017, all AusDM proceedings have been published in Springers Communication in Computer and Information Science (CCIS). CCIS is abstracted/indexed in DBLP, Google Scholar, EI-Compendex, Mathematical Reviews, SCImago, Scopus. Previous AusDM proceedings (2006 to 2016) have been published as volumes in the Conferences in Research and Practice in Information Technology (CRPIT) series.

Please note that AusDM’19 requires that at least one author for each accepted paper register for the conference and present their work, and one registration covers only one paper.

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

We invite three types of submissions for AusDM’19:

• Research Track: Academic submissions reporting on new algorithms, novel approaches and research progress, with a paper length of between 8 and 12 pages in CCIS style.
• 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 12 pages in CCIS style.
• 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 in CCIS style.

Detailed requirements on formatting are available on the Submission page