ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities
This addresses a limitation in clustering algorithms for data with non-uniform densities, but it is incremental as it builds directly on DBSCAN.
The authors tackled the problem of DBSCAN's reduced performance on clusters with varying densities by proposing an adaptive version, ADBSCAN, which works significantly well for identifying such clusters.
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.