Package elki.clustering
Clustering algorithms.
Clustering algorithms are supposed to implement the
Algorithm
-Interface.
The more specialized interface
ClusteringAlgorithm
requires an implementing algorithm to provide a special result class suitable
as a partitioning of the database. More relaxed clustering algorithms are
allowed to provide a result that is a fuzzy clustering, does not partition
the database complete or is in any other sense a relaxed clustering result.
- See Also:
elki.algorithm
-
Interface Summary Interface Description ClusteringAlgorithm<C extends Clustering<? extends Model>> Interface for Algorithms that are capable to provide aClustering
as Result. in general, clustering algorithms are supposed to implement theAlgorithm
-Interface. -
Class Summary Class Description AbstractProjectedClustering<R extends Clustering<?>> AbstractProjectedClustering.Par Parameterization class.BetulaLeafPreClustering BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.BetulaLeafPreClustering.Par Parameterization class.CanopyPreClustering<O> Canopy pre-clustering is a simple preprocessing step for clustering.CFSFDP<O> Clustering by fast search and find of density peaks (CFSFDP) is a density-based clustering method similar to mean-shift clustering.CFSFDP.Par<O> ParameterizerClusteringAlgorithmUtil Utility functionality for writing clustering algorithms.Leader<O> Leader clustering algorithm.NaiveMeanShiftClustering<V extends NumberVector> Mean-shift based clustering algorithm.SNNClustering<O> Shared nearest neighbor clustering.