Individual Fair Density-Peaks Clustering Based on Local Similar Center Graph and Similar Decision Matrix

Yiding Tang, Zhijing Yang, Yufan Peng, Hui Zhang

Abstract: Clustering, as a core technique in data mining, plays a crucial role in uncovering latent patterns in data. Among the many clustering algorithms, Density-Peaks Clustering (DPC) has garnered significant attention due to its ability to efficiently form high-density clusters. Recent research has primarily focused on improving the accuracy and speed of DPC. As concerns about fairness in data science continue to grow, clustering algorithms have gradually started incorporating fairness constraints. Nevertheless, DPC and its variants have remained largely unexplored from the perspective of fairness. Consequently, this paper proposes a novel algorithm, Individual Fair Density-Peaks Clustering (IFDPC), which enhancing individual fairness by Local Similar Center Graph (LSCG), dynamically assigning rest data based on updating Similar Decision Matrix. Experimental results demonstrate that, compared to DPC and its variants, IFDPC not only achieves better fairness but also delivers comparable clustering performance. This work is the first attempt to introduce individual fairness in DPC even in density-based clustering. Code is available on https://github.com/HurryUp1234/IFDPC.