AutoSCAN: automatic detection of DBSCAN parameters and efficient clustering of data in overlapping density regions.

ABSTRACT: The density-based clustering method is considered a robust approach in unsupervised clustering technique due to its ability to identify outliers, form clusters of irregular shapes and automatically determine the number of clusters. These unique properties helped its pioneering algorithm, the Density-based Spatial Clustering on Applications with Noise (DBSCAN), become applicable in datasets where various number of clusters of different shapes and sizes could be detected without much interference from the user. However, the original algorithm exhibits limitations, especially towards its sensitivity on its user input parameters minPts and ɛ. Additionally, the algorithm assigned inconsistent cluster labels to data objects found in overlapping density regions of separate clusters, hence

SUBMITTER: Bushra AA 

PROVIDER: S-EPMC11042006 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature