jsat.clustering.kmeans

Class HamerlyKMeans

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.kmeans.KMeans

jsat.clustering.kmeans.HamerlyKMeans

All Implemented Interfaces:

java.io.Serializable, Clusterer, KClusterer, Parameterized

public class HamerlyKMeans
extends KMeans

An efficient implementation of the K-Means algorithm. This implementation uses the triangle inequality to accelerate computation while maintaining the exact same solution. This requires that the DistanceMetric used support DistanceMetric.isSubadditive(). It uses only O(n) extra memory.

See:

• Hamerly, G. (2010). Making k-means even faster. SIAM International Conference on Data Mining (SDM) (pp. 130–140). Retrieved from here
• Ryšavý, P., & Hamerly, G. (2016). Geometric methods to accelerate k-means algorithms. In Proceedings of the 2016 SIAM International Conference on Data Mining (pp. 324–332). Philadelphia, PA: Society for Industrial and Applied Mathematics. http://doi.org/10.1137/1.9781611974348.37

See Also:

Serialized Form

Field Summary

Fields inherited from class jsat.clustering.kmeans.KMeans

DEFAULT_SEED_SELECTION

Constructor Summary

Constructors

Constructor and Description
HamerlyKMeans() Creates a new k-Means object
HamerlyKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection) Creates a new k-Means object
HamerlyKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, java.util.Random rand) Creates a new k-Means object
HamerlyKMeans(HamerlyKMeans toCopy)

Method Summary

Modifier and Type	Method and Description
HamerlyKMeans	clone()

Methods inherited from class jsat.clustering.kmeans.KMeans

cluster, cluster, cluster, cluster, getDistanceMetric, getIterationLimit, getMeans, getSeedSelection, setIterationLimit, setSeedSelection, setStoreMeans, supportsWeightedData

Methods inherited from class jsat.clustering.ClustererBase

createClusterListFromAssignmentArray, getDatapointsFromCluster

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Methods inherited from interface jsat.clustering.KClusterer

cluster, cluster, cluster, cluster, cluster, cluster

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster

Constructor Detail

HamerlyKMeans

public HamerlyKMeans(DistanceMetric dm,
                     SeedSelectionMethods.SeedSelection seedSelection,
                     java.util.Random rand)

Creates a new k-Means object

Parameters:

dm - the distance metric to use for clustering

seedSelection - the method of initial seed selection

rand - the source of randomnes to use

HamerlyKMeans

public HamerlyKMeans(DistanceMetric dm,
                     SeedSelectionMethods.SeedSelection seedSelection)

Creates a new k-Means object

Parameters:

dm - the distance metric to use for clustering

seedSelection - the method of initial seed selection

HamerlyKMeans

public HamerlyKMeans()

Creates a new k-Means object

HamerlyKMeans

public HamerlyKMeans(HamerlyKMeans toCopy)

Method Detail

clone

public HamerlyKMeans clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KMeans