Deep fair clustering with multi-objective handling

Abstract

Clustering is an unsupervised learning technique widely used in various critical domains, such as heathcare and finance, yet fairness remains an underexplored challenge especially in the field of deep-learning based clustering. Traditional methods often reinforce biases present in the dataset, leading to ethical concerns in critical applications This thesis proposes Deep Fair Clustering with Multi-Objective Handling (DFC-MOH), a deep learning framework integrating both group level and individual Level fairness constraints with clustering quality. By incorporating balance loss for group fairness and individual fairness loss into a combined loss function, DFC-MOH enables flexible trade-offs between both fairness constraints and clustering performance. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of our approach in achieving fair and high-quality clustering with reasonable scalability.