Density Peak Clustering: A Systematic Survey of Methods, Applications, Challenges, and Future Research Directions

Density Peak Clustering (DPC) is a widely studied unsupervised clustering method that identifies cluster centers using local density and relative separation without iterative prototype updates. Its simple and intuitive formulation has motivated extensive research, but its performance remains sensitive to data representation, distance measures, neighborhood construction, density estimation, center selection, assignment strategies, and data scale. To provide an updated and structured view of the field, this paper presents a systematic mapping review of DPC research published from 2014 to 8 August 2026. The survey first establishes the fundamental concepts, original DPC procedure, methodological evolution, and current methodological landscape. It then examines major application areas, including healthcare, cybersecurity, industrial monitoring, remote sensing and image analysis, transportation, networks, text analysis, multivariate time series, and scientific data. Subsequently, the methodological advances enabling modern DPC are organized into eight families: adaptive neighborhood and density estimation; graph, manifold, and multi-peak modeling; automatic center and cluster-number determination; assignment, merging, and uncertainty handling; representation learning; privacy-preserving, federated, and incremental computation; scalable execution; and auxiliary strategies such as sampling, grids, optimization, and limited supervision. Each methodological family is related to the limitation it addresses and the assumptions or computational costs it introduces. Finally, the survey synthesizes key lessons, research potential, persistent challenges, and future directions toward robust, interpretable, fairness-aware, privacy-preserving, reproducible, resource-efficient, uncertainty-aware, and continually adaptive DPC systems.