Which 1996 clustering algorithm is density-based and can identify outliers as 'noise' points?
Show answer
DBSCAN
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) groups points that are closely packed together while marking points in low-density regions as outliers. Unlike K-means, it does not require the user to specify the number of clusters in advance.
Also asked as:
- Which density-based clustering algorithm groups closely packed points and marks those in low-density regions as outliers?