AgglomerativeClusterer (Cognitive Foundry)

java.lang.Object
- gov.sandia.cognition.util.AbstractCloneableSerializable
- - gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
  - - gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>
    - - gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType>>
      - gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer<DataType,ClusterType>

Type Parameters:

DataType - The type of the data to cluster. This is typically defined by the divergence function used.

ClusterType - The type of Cluster created by the algorithm. This is typically defined by the cluster creator function used.

All Implemented Interfaces:

AnytimeAlgorithm<java.util.Collection<ClusterType>>, IterativeAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType>>, BatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType>>, BatchClusterer<DataType,ClusterType>, BatchHierarchicalClusterer<DataType,ClusterType>, DivergenceFunctionContainer<ClusterType,ClusterType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable
```
@CodeReview(reviewer="Kevin R. Dixon",
            date="2008-07-22",
            changesNeeded=true,
            comments={"I *really* don\'t like the use of \'continue\', but I will defer.","Please implement the sections previously marked as \'to do\'"},
            response=@CodeReviewResponse(respondent="Justin Basilico",date="2008-10-07",moreChangesNeeded=false,comments="The clusterer now supports hierarchical clustering."))
public class AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>>
extends AbstractAnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType>>
implements BatchClusterer<DataType,ClusterType>, BatchHierarchicalClusterer<DataType,ClusterType>, DivergenceFunctionContainer<ClusterType,ClusterType>
```
The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm. Such a clustering algorithm works by initially creating one cluster for each element in the collection to cluster and then repeatedly merging the two closest clusters until the stopping condition is met or there is only one cluster remaining. This implementation supports multiple methods for determining the distance between two clusters by supplying an ClusterToClusterDivergenceFunction object. There are two stopping conditions for the algorithm, which are parameters that can be set. The first is that the clustering will stop when some minimum number of clusters is reached, which defaults to 1. The second criteria is that the clustering will stop when the distance between the two closest clusters is larger than a given value. This threshold can be used to create clusters when the number of clusters is not known ahead of time.

Since:

1.0

Author:

Justin Basilico

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`AgglomerativeClusterer.HierarchyNode<DataType,ClusterType extends Cluster<DataType>>` Holds the hierarchy information for the agglomerative clusterer.

Field Summary

Fields
Modifier and Type	Field and Description
`protected java.util.ArrayList<ClusterType>`	`clusters` The current set of clusters.
`protected java.util.ArrayList<AgglomerativeClusterer.HierarchyNode<DataType,ClusterType>>`	`clustersHierarchy` The current set of hierarchical clusters.
`protected ClusterCreator<ClusterType,DataType>`	`creator` The merger used to merge two clusters into one element.
`static double`	`DEFAULT_MAX_DISTANCE` The default maximum distance is 1.7976931348623157E308.
`static int`	`DEFAULT_MAX_ITERATIONS` The default maximum number of iterations 2147483647
`static double`	`DEFAULT_MAX_MIN_DISTANCE` Deprecated.
`static int`	`DEFAULT_MIN_NUM_CLUSTERS` The default minimum number of clusters is 1.
`protected ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType>`	`divergenceFunction` The divergence function used to find the distance between two clusters.
`protected double`	`maxDistance` The maximum distance between clusters allowed.
`protected java.util.ArrayList<java.lang.Integer>`	`minClusters` The array of indexes that maps the cluster index to the closest cluster.
`protected java.util.ArrayList<java.lang.Double>`	`minDistances` An array list mapping the cached minimum distance from the cluster with the given index to any other clusters.
`protected int`	`minNumClusters` The minimum number of clusters allowed.

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner
data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm
maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
DEFAULT_ITERATION, iteration

Constructor Summary

Constructors
Constructor and Description
`AgglomerativeClusterer()` Creates a new instance of AgglomerativeClusterer.
`AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator)` Initializes the clustering to use the given metric between clusters, and the given cluster creator.
`AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, double maxDistance)` Initializes the clustering to use the given metric between clusters, the given cluster merger, and the maximum distance between clusters to allow when merging.
`AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, int minNumClusters)` Initializes the clustering to use the given metric between clusters, the given cluster creator, and the minimum number of clusters to allow.
`AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, int minNumClusters, double maxDistance)` Initializes the clustering to use the given metric between clusters, the given cluster merger, the minimum number of clusters to allow, and the maximum minimum distance between clusters to allow.

Method Summary

All Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`protected void`	`cleanupAlgorithm()` Called to clean up the learning algorithm's state after learning has finished.
`AgglomerativeClusterer<DataType,ClusterType>`	`clone()` This makes public the clone method on the `Object` class and removes the exception that it throws.
`ClusterHierarchyNode<DataType,ClusterType>`	`clusterHierarchically(java.util.Collection<? extends DataType> data)` Performs hierarchical clustering on the given elements.
`java.util.ArrayList<AgglomerativeClusterer.HierarchyNode<DataType,ClusterType>>`	`getClustersHierarchy()` Gets the hierarchy of clusters.
`ClusterCreator<ClusterType,DataType>`	`getCreator()` Gets the cluster creator.
`ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType>`	`getDivergenceFunction()` Gets the divergence function used in clustering.
`double`	`getMaxDistance()` The maximum distance between clusters that is allowed for the two clusters to be merged.
`double`	`getMaxMinDistance()` Deprecated. Use getMaxDistance
`int`	`getMinNumClusters()` The minimum number of clusters to allow.
`int`	`getNumClusters()` Gets the number of clusters.
`java.util.ArrayList<ClusterType>`	`getResult()` Gets the current result of the algorithm.
`protected boolean`	`initializeAlgorithm()` Called to initialize the learning algorithm's state based on the data that is stored in the data field.
`protected int`	`mergeClusters(int firstIndex, int secondIndex, double distance)` Merges two clusters together, creating a new BinaryTreeCluster and updating the internal state.
`protected void`	`setClusters(java.util.ArrayList<ClusterType> clusters)` Sets the clusters.
`protected void`	`setClustersHierarchy(java.util.ArrayList<AgglomerativeClusterer.HierarchyNode<DataType,ClusterType>> clustersHierarchy)` Sets the hierarchy of clusters.
`void`	`setCreator(ClusterCreator<ClusterType,DataType> creator)` Sets the cluster creator.
`void`	`setDivergenceFunction(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction)` Sets the divergence function.
`void`	`setMaxDistance(double maxDistance)` The maximum distance between clusters that is allowed for the two clusters to be merged.
`void`	`setMaxMinDistance(double maxMinDistance)` Deprecated. Use setMaxDistance
`protected void`	`setMinClusters(java.util.ArrayList<java.lang.Integer> minClusters)` Sets the index of the closest cluster.
`protected void`	`setMinDistances(java.util.ArrayList<java.lang.Double> minDistances)` Sets the minimum distances for each clusters.
`void`	`setMinNumClusters(int minNumClusters)` The minimum number of clusters to allow.
`protected boolean`	`step()` Called to take a single step of the learning algorithm.
`protected void`	`updateMinDistance(int index)` Updates the cached minimum distance for this cluster by comparing it to all the other clusters.

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner
getData, getKeepGoing, learn, setData, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm
getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner
learn

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm
getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm
addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm
isResultValid

- Field Detail
  - DEFAULT_MIN_NUM_CLUSTERS
```
public static final int DEFAULT_MIN_NUM_CLUSTERS
```
    The default minimum number of clusters is 1.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_MAX_DISTANCE
```
public static final double DEFAULT_MAX_DISTANCE
```
    The default maximum distance is 1.7976931348623157E308.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_MAX_MIN_DISTANCE
```
@Deprecated
public static final double DEFAULT_MAX_MIN_DISTANCE
```
    Deprecated.
    
    The default maximum minimum distance is 1.7976931348623157E308.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_MAX_ITERATIONS
```
public static final int DEFAULT_MAX_ITERATIONS
```
    The default maximum number of iterations 2147483647
    
    See Also:
    
    Constant Field Values
  - divergenceFunction
```
protected ClusterToClusterDivergenceFunction<? super ClusterType extends Cluster<DataType>,? super DataType> divergenceFunction
```
    The divergence function used to find the distance between two clusters.
  - creator
```
protected ClusterCreator<ClusterType extends Cluster<DataType>,DataType> creator
```
    The merger used to merge two clusters into one element.
  - minNumClusters
```
protected int minNumClusters
```
    The minimum number of clusters allowed.
  - maxDistance
```
protected double maxDistance
```
    The maximum distance between clusters allowed.
  - clusters
```
protected java.util.ArrayList<ClusterType extends Cluster<DataType>> clusters
```
    The current set of clusters.
  - clustersHierarchy
```
protected java.util.ArrayList<AgglomerativeClusterer.HierarchyNode<DataType,ClusterType extends Cluster<DataType>>> clustersHierarchy
```
    The current set of hierarchical clusters.
  - minDistances
```
protected transient java.util.ArrayList<java.lang.Double> minDistances
```
    An array list mapping the cached minimum distance from the cluster with the given index to any other clusters.
  - minClusters
```
protected transient java.util.ArrayList<java.lang.Integer> minClusters
```
    The array of indexes that maps the cluster index to the closest cluster.
- Constructor Detail
  - AgglomerativeClusterer
```
public AgglomerativeClusterer()
```
    Creates a new instance of AgglomerativeClusterer.
  - AgglomerativeClusterer
```
public AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction,
                              ClusterCreator<ClusterType,DataType> creator)
```
    Initializes the clustering to use the given metric between clusters, and the given cluster creator. The minimum number of clusters will be set to 1.
    
    Parameters:
    
    divergenceFunction - The distance metric between clusters.
    
    creator - The method for creating clusters.
  - AgglomerativeClusterer
```
public AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction,
                              ClusterCreator<ClusterType,DataType> creator,
                              int minNumClusters)
```
    Initializes the clustering to use the given metric between clusters, the given cluster creator, and the minimum number of clusters to allow.
    
    Parameters:
    
    divergenceFunction - The distance metric between clusters.
    
    creator - The method for creating clusters.
    
    minNumClusters - The minimum number of clusters to allow. Must be greater than zero.
  - AgglomerativeClusterer
```
public AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction,
                              ClusterCreator<ClusterType,DataType> creator,
                              double maxDistance)
```
    Initializes the clustering to use the given metric between clusters, the given cluster merger, and the maximum distance between clusters to allow when merging.
    
    Parameters:
    
    divergenceFunction - The distance metric between clusters.
    
    creator - The method for creating clusters.
    
    maxDistance - The maximum distance between clusters to allow when merging them.
  - AgglomerativeClusterer
```
public AgglomerativeClusterer(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction,
                              ClusterCreator<ClusterType,DataType> creator,
                              int minNumClusters,
                              double maxDistance)
```
    Initializes the clustering to use the given metric between clusters, the given cluster merger, the minimum number of clusters to allow, and the maximum minimum distance between clusters to allow.
    
    Parameters:
    
    divergenceFunction - The distance metric between clusters.
    
    creator - The method for creating clusters.
    
    minNumClusters - The minimum number of clusters to allow. Must be greater than zero.
    
    maxDistance - The maximum distance between clusters to allow when merging them.
- Method Detail
  - clone
```
public AgglomerativeClusterer<DataType,ClusterType> clone()
```
    Description copied from class: AbstractCloneableSerializable
    
    This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.
    
    Specified by:
    
    clone in interface CloneableSerializable
    
    Overrides:
    
    clone in class AbstractAnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType extends Cluster<DataType>>>
    
    Returns:
    
    A clone of this object.
  - clusterHierarchically
```
public ClusterHierarchyNode<DataType,ClusterType> clusterHierarchically(java.util.Collection<? extends DataType> data)
```
    Description copied from interface: BatchHierarchicalClusterer
    
    Performs hierarchical clustering on the given elements. It returns the root node of the hierarchy of clusters for the given elements.
    
    Specified by:
    
    clusterHierarchically in interface BatchHierarchicalClusterer<DataType,ClusterType extends Cluster<DataType>>
    
    Parameters:
    
    data - The elements to cluster.
    
    Returns:
    
    The root node of the hierarchical cluster for the given elements.
  - initializeAlgorithm
```
protected boolean initializeAlgorithm()
```
    Description copied from class: AbstractAnytimeBatchLearner
    
    Called to initialize the learning algorithm's state based on the data that is stored in the data field. The return value indicates if the algorithm can be run or not based on the initialization.
    
    Specified by:
    
    initializeAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType extends Cluster<DataType>>>
    
    Returns:
    
    True if the learning algorithm can be run and false if it cannot.
  - step
```
protected boolean step()
```
    Description copied from class: AbstractAnytimeBatchLearner
    
    Called to take a single step of the learning algorithm.
    
    Specified by:
    
    step in class AbstractAnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType extends Cluster<DataType>>>
    
    Returns:
    
    True if another step can be taken and false it the algorithm should halt.
  - cleanupAlgorithm
```
protected void cleanupAlgorithm()
```
    Description copied from class: AbstractAnytimeBatchLearner
    
    Called to clean up the learning algorithm's state after learning has finished.
    
    Specified by:
    
    cleanupAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType extends Cluster<DataType>>>
  - updateMinDistance
```
protected void updateMinDistance(int index)
```
    Updates the cached minimum distance for this cluster by comparing it to all the other clusters.
    
    Parameters:
    
    index - The cluster to update.
  - mergeClusters
```
protected int mergeClusters(int firstIndex,
                            int secondIndex,
                            double distance)
```
    Merges two clusters together, creating a new BinaryTreeCluster and updating the internal state.
    
    Parameters:
    
    firstIndex - The first cluster.
    
    secondIndex - The second cluster.
    
    distance - The distance between the clusters.
    
    Returns:
    
    The new, merged cluster.
  - getResult
```
public java.util.ArrayList<ClusterType> getResult()
```
    Description copied from interface: AnytimeAlgorithm
    
    Gets the current result of the algorithm.
    
    Specified by:
    
    getResult in interface AnytimeAlgorithm<java.util.Collection<ClusterType extends Cluster<DataType>>>
    
    Returns:
    
    Current result of the algorithm.
  - getNumClusters
```
public int getNumClusters()
```
    Gets the number of clusters.
    
    Returns:
    
    The number of clusters.
  - getDivergenceFunction
```
public ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> getDivergenceFunction()
```
    Gets the divergence function used in clustering.
    
    Specified by:
    
    getDivergenceFunction in interface DivergenceFunctionContainer<ClusterType extends Cluster<DataType>,ClusterType extends Cluster<DataType>>
    
    Returns:
    
    The divergence function.
  - setDivergenceFunction
```
public void setDivergenceFunction(ClusterToClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction)
```
    Sets the divergence function.
    
    Parameters:
    
    divergenceFunction - The divergence function.
  - getCreator
```
public ClusterCreator<ClusterType,DataType> getCreator()
```
    Gets the cluster creator.
    
    Returns:
    
    The cluster creator.
  - setCreator
```
public void setCreator(ClusterCreator<ClusterType,DataType> creator)
```
    Sets the cluster creator.
    
    Parameters:
    
    creator - The creator for clusters.
  - getMinNumClusters
```
public int getMinNumClusters()
```
    The minimum number of clusters to allow. To create a cluster tree, set this value to 1. If the number of clusters drops to this number (or below) then the clustering will stop. Must be greater than zero.
    
    Returns:
    
    The minimum number of clusters allowed.
  - setMinNumClusters
```
public void setMinNumClusters(int minNumClusters)
```
    The minimum number of clusters to allow. To create a cluster tree, set this value to 1. If the number of clusters drops to this number (or below) then the clustering will stop. Must be greater than zero.
    
    Parameters:
    
    minNumClusters - The new minimum number of clusters.
  - getMaxMinDistance
```
@Deprecated
public double getMaxMinDistance()
```
    Deprecated. Use getMaxDistance
    
    Gets the maximum distance.
    
    Returns:
    
    The maximum distance.
  - setMaxMinDistance
```
@Deprecated
public void setMaxMinDistance(double maxMinDistance)
```
    Deprecated. Use setMaxDistance
    
    Sets the maximum distance.
    
    Parameters:
    
    maxMinDistance - The maximum distance.
  - getMaxDistance
```
public double getMaxDistance()
```
    The maximum distance between clusters that is allowed for the two clusters to be merged. If there are no clusters that remain that have a distance between them less than or equal to this value, then the clustering will halt. To not have this value factored into the clustering, set it to something such as Double.MAX_VALUE.
    
    Returns:
    
    The maximum distance between clusters to merge.
  - setMaxDistance
```
public void setMaxDistance(double maxDistance)
```
    The maximum distance between clusters that is allowed for the two clusters to be merged. If there are no clusters that remain that have a distance between them less than or equal to this value, then the clustering will halt. To not have this value factored into the clustering, set it to something such as Double.MAX_VALUE.
    
    Parameters:
    
    maxDistance - The new maximum distance between clusters to merge.
  - setClusters
```
protected void setClusters(java.util.ArrayList<ClusterType> clusters)
```
    Sets the clusters.
    
    Parameters:
    
    clusters - The clusters.
  - getClustersHierarchy
```
public java.util.ArrayList<AgglomerativeClusterer.HierarchyNode<DataType,ClusterType>> getClustersHierarchy()
```
    Gets the hierarchy of clusters.
    
    Returns:
    
    The hierarchy of clusters.
  - setClustersHierarchy
```
protected void setClustersHierarchy(java.util.ArrayList<AgglomerativeClusterer.HierarchyNode<DataType,ClusterType>> clustersHierarchy)
```
    Sets the hierarchy of clusters.
    
    Parameters:
    
    clustersHierarchy - The hierarchy of clusters.
  - setMinDistances
```
protected void setMinDistances(java.util.ArrayList<java.lang.Double> minDistances)
```
    Sets the minimum distances for each clusters.
    
    Parameters:
    
    minDistances - The array of minimum distances.
  - setMinClusters
```
protected void setMinClusters(java.util.ArrayList<java.lang.Integer> minClusters)
```
    Sets the index of the closest cluster.
    
    Parameters:
    
    minClusters - The array of closest cluster indices.

Class AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>>

Nested Class Summary

Field Summary

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

Constructor Summary

Method Summary

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

Methods inherited from class java.lang.Object

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

Field Detail

DEFAULT_MIN_NUM_CLUSTERS

DEFAULT_MAX_DISTANCE

DEFAULT_MAX_MIN_DISTANCE

DEFAULT_MAX_ITERATIONS

divergenceFunction

creator

minNumClusters

maxDistance

clusters

clustersHierarchy

minDistances

minClusters

Constructor Detail

AgglomerativeClusterer

AgglomerativeClusterer

AgglomerativeClusterer

AgglomerativeClusterer

AgglomerativeClusterer

Method Detail

clone

clusterHierarchically

initializeAlgorithm

step

cleanupAlgorithm

updateMinDistance

mergeClusters

getResult

getNumClusters

getDivergenceFunction

setDivergenceFunction

getCreator

setCreator

getMinNumClusters

setMinNumClusters

getMaxMinDistance

setMaxMinDistance

getMaxDistance

setMaxDistance

setClusters

getClustersHierarchy

setClustersHierarchy

setMinDistances

setMinClusters