GrDBSCAN: A granular density-based clustering algorithm - Suchy, Dawid; Simiński, Krzysztof (Zielona Góra: Uniwersytet Zielonogórski)
Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback - its worst-case computational complexity is O(n2) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity.
Keywords: granular computing; DBSCAN; clustering; GrDBSCAN