| Interface | Description |
|---|---|
| CorePredicate |
Predicate for GeneralizedDBSCAN to evaluate whether a point is a core point
or not.
|
| CorePredicate.Instance<T> |
Instance for a particular data set.
|
| NeighborPredicate |
Get the neighbors of an object
Note the Factory/Instance split of this interface.
|
| NeighborPredicate.Instance<T> |
Instance for a particular data set.
|
| Class | Description |
|---|---|
| EpsilonNeighborPredicate<O,D extends Distance<D>> |
The default DBSCAN and OPTICS neighbor predicate, using an
epsilon-neighborhood.
|
| EpsilonNeighborPredicate.Instance<D extends Distance<D>> |
Instance for a particular data set.
|
| EpsilonNeighborPredicate.Parameterizer<O,D extends Distance<D>> |
Parameterization class
|
| GeneralizedDBSCAN |
Generalized DBSCAN, density-based clustering with noise.
|
| GeneralizedDBSCAN.Parameterizer |
Parameterization class
|
| MinPtsCorePredicate |
The DBSCAN default core point predicate -- having at least
MinPtsCorePredicate.minpts
neighbors. |
| MinPtsCorePredicate.Instance |
Instance for a particular data set.
|
| MinPtsCorePredicate.Parameterizer |
Parameterization class
|
Generalized DBSCAN.
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, Hans-Peter Kriegel, Xiaowei Xu:
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its
Applications
In: Data Mining and Knowledge Discovery, 1998.