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 ...
GitHub

Theoretically-Efficient and Practical Parallel DBSCAN

This repository hosts fast parallel DBSCAN clustering code for low dimensional Euclidean space. The code automatically uses the available threads on a parallel shared-memory machine to speedup DBSCAN ...
IEEE

RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates

Abstract: A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse nearest neighbor counts as an estimate of observation density. Clustering is performed using a ...
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 ...