| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.clustering |
Provides clustering algorithms.
|
| gov.sandia.cognition.learning.function.vector |
Provides functions that output vectors.
|
| gov.sandia.cognition.statistics.distribution |
Provides statistical distributions.
|
| Modifier and Type | Class and Description |
|---|---|
class |
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.
|
class |
MiniBatchKMeansClusterer<DataType extends Vector>
Approximates k-means clustering by working on random subsets of the
data.
|
class |
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.
|
class |
ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
This is a parallel implementation of the k-means clustering algorithm.
|
| Modifier and Type | Method and Description |
|---|---|
KMeansClusterer<DataType,ClusterType> |
KMeansClusterer.clone() |
| Constructor and Description |
|---|
Learner(KMeansClusterer<Vector,GaussianCluster> algorithm)
Creates a new instance of Learner
|
| Constructor and Description |
|---|
Learner(KMeansClusterer<Vector,GaussianCluster> algorithm)
Creates a new Learner
|