See: Description
Interface | Description |
---|---|
BatchClusterer<DataType,ClusterType extends Cluster<DataType>> |
The
BatchClusterer interface defines the functionality of a batch
clustering algorithm. |
Class | Description |
---|---|
AffinityPropagation<DataType> |
The
AffinityPropagation algorithm requires three parameters:
a divergence function, a value to use for self-divergence, and a damping
factor (called lambda in the paper; 0.5 is the default). |
AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>> |
The
AgglomerativeClusterer implements an agglomerative clustering
algorithm, which is a type of hierarchical clustering algorithm. |
AgglomerativeClusterer.HierarchyNode<DataType,ClusterType extends Cluster<DataType>> |
Holds the hierarchy information for the agglomerative clusterer.
|
DBSCANClusterer<DataType extends Vectorizable,ClusterType extends Cluster<DataType>> |
The
DBSCAN algorithm requires three parameters: a distance
metric, a value for neighborhood radius, and a value for the minimum number
of surrounding neighbors for a point to be considered non-noise. |
DirichletProcessClustering |
Clustering algorithm that wraps Dirichlet Process Mixture Model.
|
KMeansClusterer<DataType,ClusterType extends Cluster<DataType>> |
The
KMeansClusterer class implements the standard k-means
(k-centroids) clustering algorithm. |
KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>> |
Creates a k-means clustering algorithm that removes clusters that do
not have sufficient membership to pass a simple statistical significance
test.
|
KMeansFactory |
Creates a parallelized version of the k-means clustering algorithm for the
typical use: clustering vector data with a Euclidean distance metric.
|
MiniBatchKMeansClusterer<DataType extends Vector> |
Approximates k-means clustering by working on random subsets of the
data.
|
MiniBatchKMeansClusterer.Builder<DataType extends Vector> |
Can be used to create custom
MiniBatchKMeansClusterer s without
using the big constructor. |
OptimizedKMeansClusterer<DataType> |
This class implements an optimized version of the k-means algorithm that
makes use of the triangle inequality to compute the same answer as k-means
while using less distance calculations.
|
ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>> |
This is a parallel implementation of the k-means clustering algorithm.
|
PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>> |
The
PartitionalClusterer implements a partitional clustering
algorithm, which is a type of hierarchical clustering algorithm. |