Parameter Free Clustering Algorithm

Parameter-free clustering algorithms aim to group data points without requiring user-specified parameters, addressing a major limitation of traditional methods. Recent research focuses on developing such algorithms using diverse approaches, including persistent homology (e.g., AuToMATo), analysis of latent spaces in vision models, and novel techniques for handling missing data (e.g., Single-Dimensional Clustering). These advancements improve the robustness and generalizability of clustering, leading to more reliable and efficient data analysis across various applications, particularly in scenarios where parameter tuning is difficult or impractical.