Package elki.clustering.dbscan
DBSCAN and its generalizations.
Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
For each object, the neighborhood as defined by the "neighborhood" predicate is retrieved  in original DBSCAN, this is the objects within an epsilon sphere around the query object. Then the core point predicate is evaluated to decide if the object is considered dense. If so, a cluster is started (or extended) to include the neighbors as well.
Reference:
Jörg Sander, Martin Ester, HansPeter Kriegel, Xiaowei Xu
DensityBased Clustering in Spatial Databases:
The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery, 1998.

Class Summary Class Description DBSCAN<O> DensityBased Clustering of Applications with Noise (DBSCAN), an algorithm to find densityconnected sets in a database.DBSCAN.Par<O> Parameterization class.GeneralizedDBSCAN Generalized DBSCAN, densitybased clustering with noise.GeneralizedDBSCAN.Instance<T> Instance for a particular data set.GeneralizedDBSCAN.Par Parameterization classGriDBSCAN<V extends NumberVector> Using Grid for Accelerating DensityBased Clustering.GriDBSCAN.Instance<V extends NumberVector> Instance, for a single run.LSDBC<O extends NumberVector> Locally Scaled Density Based Clustering.LSDBC.Par<O extends NumberVector> Parameterization class