public class DivisiveGlobalClusterer extends KClustererBase
SimpleHAC
, top-down methods require another
clustering method to perform the splitting at each iteration. If the base
method is not deterministic, then the top-down method will not be
deterministic.
ClusterEvaluation
used. This quality
comes at the cost of execution speed, as more and more large evaluations of
the whole dataset are needed at each iteration. If execution speed is more
important, DivisiveLocalClusterer
should be used instead, which
requires only a fixed number of evaluations per iteration.Constructor and Description |
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DivisiveGlobalClusterer(DivisiveGlobalClusterer toCopy)
Copy constructor
|
DivisiveGlobalClusterer(KClusterer baseClusterer,
ClusterEvaluation clusterEvaluation) |
Modifier and Type | Method and Description |
---|---|
DivisiveGlobalClusterer |
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.
|
int[] |
cluster(DataSet dataSet,
int clusters,
ExecutorService threadpool,
int[] designations) |
int[] |
cluster(DataSet dataSet,
int clusters,
int[] designations) |
int[] |
cluster(DataSet dataSet,
int lowK,
int highK,
ExecutorService threadpool,
int[] designations) |
int[] |
cluster(DataSet dataSet,
int lowK,
int highK,
int[] designations) |
int[] |
clusterSplit(int targetK)
Returns the clustering results for a specific k number of clusters
for a previously computed data set.
|
cluster, cluster, cluster, cluster
cluster, cluster, createClusterListFromAssignmentArray, getDatapointsFromCluster, supportsWeightedData
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
cluster, cluster, supportsWeightedData
public DivisiveGlobalClusterer(KClusterer baseClusterer, ClusterEvaluation clusterEvaluation)
public DivisiveGlobalClusterer(DivisiveGlobalClusterer toCopy)
toCopy
- the object to copypublic int[] cluster(DataSet dataSet, int[] designations)
Clusterer
dataSet
- the data set to perform clustering ondesignations
- 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.public int[] cluster(DataSet dataSet, ExecutorService threadpool, int[] designations)
Clusterer
dataSet
- the data set to perform clustering onthreadpool
- a source of threads to run tasksdesignations
- 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.public int[] cluster(DataSet dataSet, int clusters, ExecutorService threadpool, int[] designations)
public int[] cluster(DataSet dataSet, int clusters, int[] designations)
public int[] cluster(DataSet dataSet, int lowK, int highK, ExecutorService threadpool, int[] designations)
public int[] clusterSplit(int targetK)
targetK
- the number of clusters to get the result for.ClusterFailureException
- if no prior data set had been clusteredpublic int[] cluster(DataSet dataSet, int lowK, int highK, int[] designations)
public DivisiveGlobalClusterer clone()
clone
in interface Clusterer
clone
in interface KClusterer
clone
in class KClustererBase
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