FDBSCAN-APT: A fuzzy density-based clustering algorithm with automatic parameter tuning

Density-based clustering algorithms represent a convenient approach when the number of clusters is not known in advance and their shapes are arbitrary. Nevertheless, they are highly sensitive to the input parameter setting, especially when clusters' borders are close to each other, or even overlap. In this paper we propose FDBSCAN-APT, a fuzzy extension of the DBSCAN algorithm. FDBSCAN-APT is able to discover clusters with fuzzy overlapping borders and relies on the automatic setting of input parameters thanks to the definition of a novel heuristic based on the statistical modelling of the density distribution of objects. An extensive experimental analysis carried out on synthetic datasets shows that FDBSCAN-APT always finds reasonable parameter configurations and produces good clustering results in a variety of challenging scenarios.