Fast LDP-MST: An Efficient Density-Peak-Based Clustering Method for Large-Size Datasets

• Published in: IEEE transactions on knowledge and data engineering Vol. 35; no. 5; pp. 4767 - 4780
• Main Authors: Qiu, Teng, Li, Yong-Jie
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Clustering algorithms; Clustering methods; Complexity; Datasets; Density; Density-peak-based clustering; Directed acyclic graph; Graph theory; in-tree; large-size datasets; minimal spanning tree; Noise sensitivity; Parameter sensitivity; Robustness; Sensitivity; Time complexity; Visualization

Recently, a new density-peak-based clustering method, called clustering with local density peaks-based minimum spanning tree (LDP-MST), was proposed, which has several attractive merits, e.g., being able to detect arbitrarily shaped clusters and not very sensitive to noise and parameters. Neverthele...