A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data

• Published in: Pattern recognition Vol. 83; pp. 375 - 387
• Main Authors: Chen, Yewang, Tang, Shengyu, Bouguila, Nizar, Wang, Cheng, Du, Jixiang, Li, HaiLin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2018
• Subjects: DBSCAN; NQ-DBSCAN; ρ-Approximate DBSCAN; ρ-Approximate DBSCAN; 99-00; DBSCAN; 00-01; NQ-DBSCAN

•The underlying idea is: point p and point q should have similar neighbors, provided p and q are close to each other; given a certain eps, the closer they are, the more similar their neighbors are.•NQ-DBSCAN is an exact algorithm that may return the same result as DBSCAN if the parameters are same....