This book constitutes the proceedings of the 21st Australasian Conference on Data Science and Machine Learning, AusDM 2023, held in Auckland, New Zealand, during December 11–13, 2023.
The 20 full papers presented in this book were carefully reviewed and selected from 50 submissions.

​Research Track: Random Padding Data Augmentation.- Unsupervised Fraud Detection on Sparse Rating Networks.- Semi-Supervised Model-Based Clustering for Ordinal Data.- Damage GAN: A Generative Model for Imbalanced Data.- Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders.- Boosting QA Performance through SA-Net and AA-Net with the Read+Verify Framework.- Anomaly Detection Algorithms: Comparative Analysis and Explainability Perspectives.- Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes.- MStoCast: Multimodal Deep Network for Stock Market Forecast..- Few Shot and Transfer Learning with Manifold Distributed Datasets.- Mitigating The Adverse Effects of Long-tailed Data on Deep Learning Models.- Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression.- Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators.- Application Track: Multi-Dimensional Data Visualization for Analyzing Materials.- Law in Order: An Open Legal Citation Network for New Zealand.- Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven Approaches.- A Comparison of One-Class versus Two-Class Machine Learning Models for Wildfire Prediction in California.- Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming.- Comparison of Interpolation Techniques for Prolonged Exposure Estimation: A Case Study on Seven years of Daily Nitrogen Oxide in Greater Sydney.- Detecting Asthma Presentations from Emergency Department Notes:An Active Learning Approach.

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