Density Peak Clustering

Density Peak Clustering (DPC) is a clustering algorithm that identifies cluster centers as data points with high density and large distances to points of even higher density. Current research focuses on improving DPC's scalability and accuracy, particularly for high-dimensional and non-Euclidean data, through techniques like parallel processing, novel distance metrics (e.g., density-based distances), and data preprocessing methods (e.g., rank transformations). These advancements aim to enhance the algorithm's robustness and applicability across diverse datasets, impacting fields requiring effective unsupervised learning for complex data structures.