Research Track -- Parallel Nonlinear Dimensionality Reduction Using GPU Acceleration -- Taking the Confusion out of Multinomial Confusion Matrices and Imbalanced Classes -- Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs -- A Drift Aware Hierarchical Test based Approach for Combating Spammers in Online Social Networks -- Hospital Readmission Prediction Using Semantic Relations Between Medical Codes -- HFM++: An Enhanced Holographic Factorization Machine for Recommendation -- Deep Learning for Bias Detection: From Inception to Deployment -- Exploring Fusion Strategies in Deep Learning Models for Multi-modal Classification -- Application Track -- Chameleon: A Python Workflow Toolkit for Feature Selection -- PostMatch: A Framework for Efficient Address Matching -- Detection of Classical Cipher Types with Feature-Learning Approach -- SOMPS-Net: Attention based Social Graph Framework for Early Detection of Fake Health News -- How to Read the News: A Study of How Sentiment Effects Financial Markets -- Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland -- A Semi-Automatic Data Extraction System for Heterogeneous Data Sources: A Case Study from Cotton Industry -- Nonnegative Matrix Factorization to Understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study.

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. The papers are organized in sections on research track and application track. *Due to the COVID-19 pandemic the conference was held online. .