Yee Ling Boo - Böcker

This book constitutes the refereed proceedings of the 15th Australasian Conference on Data Mining, AusDM 2017, held in Melbourne, VIC, Australia, in August 2017.The 17 revised full papers presented together with 11 research track papers and 6 application track papers were carefully reviewed and selected from 31 submissions. The papers are organized in topical sections on clustering and classification; big data; time series; outlier detection and applications; social media and applications.

This book constitutes the refereed proceedings of the 19th Australasian Conference on Data Mining, AusDM 2021, held in Brisbane, Queensland, Australia, in December 2021.* The 16 revised full papers presented were carefully reviewed and selected from 32 submissions.

This book constitutes the refereed proceedings of the 20th Australasian Conference on Data Mining, AusDM 2022, held in Western Sydney, Australia, during December 12–15, 2022. They were organized in topical sections as ?research track and application track.

This book constitutes the proceedings of the 23rd Australasian Conference on Data Science and Machine Learning, AusDM 2025, held in Brisbane, Australia, during November 26-28, 2025.The 37 full papers presented in this book were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: (1) Federated, Adaptive and Trustworthy Machine Learning; (2) Environment, Information Security and Productivity; (3) Deep Learning Fusion and Vision; (4) Health and Social Good and (5) Knowledge-Driven and Domain Specific AI. They deal with topics around data science, machine learning and also AI in everyday life.

This book constitutes the proceedings of the 22nd Australasian Conference on Data Science and Machine Learning, AusDM 2024, held in Melbourne, Victoria, Australia, during November 25–27, 2024.The 19 full papers included in this book were carefully reviewed and selected from 47 submissions. They were organized in two topical tracks: Research Track; and Application Track.