IEEE

SS-DBSCAN: Semi-Supervised Density-Based Spatial Clustering of Applications With Noise for Meaningful Clustering in Diverse Density Data

Abstract: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised clustering algorithm designed to identify clusters of various shapes and sizes in noisy datasets by ...
GitHub

Example of DBSCAN; what it is, how to use it and why.

Is an unsupervised density-based clustering algorithm. Density-based means that the algorithm focuses on the distance between each point and it's neighbors instead of the distance to a centroid like K ...
IEEE

WOA-DBSCAN: Application of Whale Optimization Algorithm in DBSCAN Parameter Adaption

Abstract: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a classic density-based clustering method that can identify clusters of arbitrary shapes in noisy datasets. However, ...