jsat.clustering

Class LSDBC

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.LSDBC

All Implemented Interfaces:

Serializable, Clusterer, Parameterized

public class LSDBC
extends ClustererBase
implements Parameterized

A parallel implementation of Locally Scaled Density Based Clustering.

See paper:
Biçici, E., &Yuret, D. (2007). Locally scaled density based clustering. In B. Beliczynski, A. Dzielinski, M. Iwanowski,&B. Ribeiro (Eds.), Adaptive and Natural Computing Algorithms (pp. 739–748). Warsaw, Poland: Springer-Verlag.

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_ALPHA 4.0 is the default scale value used when performing clustering.
static int	DEFAULT_NEIGHBORS 15 is the default number of neighbors used when performing clustering

Constructor Summary

Constructors

Constructor and Description
LSDBC() Creates a new LSDBC clustering object using the EuclideanDistance and default parameter values.
LSDBC(DistanceMetric dm) Creates a new LSDBC clustering object using the given distance metric
LSDBC(DistanceMetric dm, double alpha) Creates a new LSDBC clustering object using the given distance metric
LSDBC(DistanceMetric dm, double alpha, int neighbors) Creates a new LSDBC clustering object using the given distance metric
LSDBC(LSDBC toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
LSDBC	clone()
int[]	cluster(DataSet dataSet, ExecutorService threadpool, int[] designations) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int[] designations) Performs clustering on the given data set.
double	getAlpha() Returns the scale value that will control how many points are added to a cluster.
int	getNeighbors() Returns the number of neighbors that will be considered when clustering data points
Parameter	getParameter(String paramName) Returns the parameter with the given name.
List<Parameter>	getParameters() Returns the list of parameters that can be altered for this learner.
void	setAlpha(double alpha) Sets the scale value that will control how many points are added to a cluster.
void	setDistanceMetric(DistanceMetric dm) Sets the distance metric used when performing clustering.
void	setNeighbors(int neighbors) Sets the number of neighbors that will be considered when clustering data points
void	setVectorCollectionFactory(VectorCollectionFactory<VecPaired<Vec,Integer>> vectorCollectionFactory) Sets the vector collection factory used for acceleration of neighbor searches.

Methods inherited from class jsat.clustering.ClustererBase

cluster, cluster, createClusterListFromAssignmentArray, getDatapointsFromCluster, supportsWeightedData

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_NEIGHBORS

public static final int DEFAULT_NEIGHBORS

15 is the default number of neighbors used when performing clustering

See Also:

setNeighbors(int), Constant Field Values

DEFAULT_ALPHA

public static final double DEFAULT_ALPHA

4.0 is the default scale value used when performing clustering.

See Also:

setAlpha(double), Constant Field Values

Constructor Detail

LSDBC

public LSDBC(DistanceMetric dm,
             double alpha,
             int neighbors)

Creates a new LSDBC clustering object using the given distance metric

Parameters:

dm - the distance metric to use

alpha - the scale factor to use when forming clusters

neighbors - the number of neighbors to consider when determining clusters

LSDBC

public LSDBC(DistanceMetric dm,
             double alpha)

Creates a new LSDBC clustering object using the given distance metric

Parameters:

dm - the distance metric to use

alpha - the scale factor to use when forming clusters

LSDBC

public LSDBC(DistanceMetric dm)

Creates a new LSDBC clustering object using the given distance metric

Parameters:

dm - the distance metric to use

LSDBC

public LSDBC()

Creates a new LSDBC clustering object using the EuclideanDistance and default parameter values.

LSDBC

public LSDBC(LSDBC toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

setVectorCollectionFactory

public void setVectorCollectionFactory(VectorCollectionFactory<VecPaired<Vec,Integer>> vectorCollectionFactory)

Sets the vector collection factory used for acceleration of neighbor searches.

Parameters:

vectorCollectionFactory - the vector collection factory to use

setDistanceMetric

public void setDistanceMetric(DistanceMetric dm)

Sets the distance metric used when performing clustering.

Parameters:

dm - the distance metric to use.

setNeighbors

public void setNeighbors(int neighbors)

Sets the number of neighbors that will be considered when clustering data points

Parameters:

neighbors - the number of neighbors the algorithm will use

getNeighbors

public int getNeighbors()

Returns the number of neighbors that will be considered when clustering data points

Returns:

the number of neighbors the algorithm will use

setAlpha

public void setAlpha(double alpha)

Sets the scale value that will control how many points are added to a cluster. Smaller values will create more, smaller clusters - and more points will be labeled as noise. Larger values causes larger and fewer clusters.

Parameters:

alpha - the scale value to use

getAlpha

public double getAlpha()

Returns the scale value that will control how many points are added to a cluster. Smaller values will create more, smaller clusters - and more points will be labeled as noise. Larger values causes larger and fewer clusters.

Returns:

the scale value to use

cluster

public int[] cluster(DataSet dataSet,
                     int[] designations)

Description copied from interface: Clusterer

Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.

Specified by:

cluster in interface Clusterer

Parameters:

dataSet - the data set to perform clustering on

designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.

Returns:

an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null

cluster

public int[] cluster(DataSet dataSet,
                     ExecutorService threadpool,
                     int[] designations)

Description copied from interface: Clusterer

Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.

Specified by:

cluster in interface Clusterer

Parameters:

dataSet - the data set to perform clustering on

threadpool - a source of threads to run tasks

designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.

Returns:

an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null

getParameters

public List<Parameter> getParameters()

Description copied from interface: Parameterized

Returns the list of parameters that can be altered for this learner.

Specified by:

getParameters in interface Parameterized

Returns:

the list of parameters that can be altered for this learner.

getParameter

public Parameter getParameter(String paramName)

Description copied from interface: Parameterized

Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.

Specified by:

getParameter in interface Parameterized

Parameters:

paramName - the name of the parameter to obtain

Returns:

the Parameter in question, or null if no such named Parameter exists.

clone

public LSDBC clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in class ClustererBase