Density Peak

Density Peak Clustering (DPC) is a clustering algorithm designed to identify clusters of arbitrary shapes and varying densities, overcoming limitations of traditional methods that struggle with non-spherical or unevenly distributed data. Current research focuses on improving DPC's scalability and robustness, particularly through the development of parallel computing methods and refined density estimation techniques, such as those employing mutual nearest neighbors or path-based valley-seeking approaches. These advancements aim to enhance the accuracy and efficiency of DPC, making it applicable to larger and more complex datasets across diverse fields, including community detection and data analysis.