Scalable fuzzy neighborhood DBSCAN

E-DBSCAN: An evidential version of the DBSCAN method

“…A survey regarding the main crisp and fuzzy density-based methods was reported by [9]. Among these fuzzy extensions, we cite Soft DBSCAN [10], Fuzzy core DBSCAN [11], Scalable fuzzy neighborhood DBSCAN [12], and the Fuzzy extensions of the DBSCAN proposed by [13].…”

Driver fixation region division–oriented clustering method based on the density-based spatial clustering of applications with noise and the mathematical morphology clustering

“…Many domestic experts and scholars had studied conventional density clustering and morphology clustering and achieved fruitful research results: Ester et al 6 proposed the density-based spatial clustering of applications with noise (DBSCAN), and this is the earliest density-based clustering method which could divide data into arbitrary shapes; Ankerst et al 7 proposed the OPTICS, and this method improved the clustering accuracy by manually defining features of clusters through reachability plots; Kryszkiewicz and Lasek, 8 Viswanath and Babu, 9 and Mimaroglu and Aksehirli 10 attempted to reduce the time complexity using the means of the triangle inequality, rough sets, and the pruning technique on bit vectors; Parker et al, 11 Smiti and Elouedi, 12 Xu et al, 13 Borah and Bhattacharyya, 14 and Dharni and Bnasal 15 had made significant progress in DBSCAN's parameter selection through different methods; Guan et al 16 proposed a neighborhood-based clustering algorithm to attempt to optimize the density-based clustering method; Postaire et al proposed the earliest morphology-based clustering method; 17 and Starovoitov, 18 Silva and Velhinho, 19 Prangnell et al, 20 Di Kaichang et al, 21 Hernandez et al, 22 and Picard and Bar-Hen 23 optimized the morphology-based clustering methods, and these methods were used in many fields such as image processing. However, there are also problems that the DBSCAN's parameter Eps seriously affects clustering results, and the low intelligent mathematical morphology clustering (MMC) needs lots of manual intervention.…”