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java.lang.Objectweka.clusterers.AbstractClusterer
weka.clusterers.RandomizableClusterer
weka.clusterers.CascadeSimpleKMeans
public class CascadeSimpleKMeans
cascade simple k means, selects the best k according to calinski-harabasz criterion analogous to: http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/cascadeKM.html see Calinski, T. and J. Harabasz. 1974. A dendrite method for cluster analysis. Commun. Stat. 3: 1-27. quoted in German: http://books.google.com/books?id=-f9Ox0p1-D4C&lpg=PA394&ots=SV3JfRIkQn&dq=Calinski%20and%20Harabasz&hl=de&pg=PA394#v=onepage&q&f=false
Constructor Summary | |
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CascadeSimpleKMeans()
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Method Summary | |
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void |
buildClusterer(Instances data)
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int |
clusterInstance(Instance instance)
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java.lang.String |
distanceFunctionTipText()
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double[] |
distributionForInstance(Instance instance)
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Capabilities |
getCapabilities()
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DistanceFunction |
getDistanceFunction()
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boolean |
getInitializeUsingKMeansPlusPlusMethod()
Get whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm (rather than the standard random selection of initial cluster centers). |
int |
getMaxIterations()
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int |
getMaxNumClusters()
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int |
getMinNumClusters()
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java.lang.String[] |
getOptions()
Gets the current settings of SimpleKMeans. |
int |
getRestarts()
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java.lang.String |
getRevision()
Returns the revision string. |
TechnicalInformation |
getTechnicalInformation()
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java.lang.String |
globalInfo()
Returns a string describing this clusterer. |
java.lang.String |
initializeUsingKMeansPlusPlusMethodTipText()
Returns the tip text for this property. |
boolean |
isManuallySelectNumClusters()
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boolean |
isPrintDebug()
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java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] args)
Main method for executing this class. |
java.lang.String |
manuallySelectNumClustersTipText()
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java.lang.String |
maxIterationsTipText()
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java.lang.String |
maxNumClustersTipText()
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java.lang.String |
minNumClustersTipText()
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int |
numberOfClusters()
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java.lang.String |
printDebugTipText()
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java.lang.String |
restartsTipText()
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void |
setDistanceFunction(DistanceFunction distanceFunction)
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void |
setInitializeUsingKMeansPlusPlusMethod(boolean k)
Set whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm (rather than the standard random selection of initial cluster centers). |
void |
setManuallySelectNumClusters(boolean manuallySelectNumClusters)
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void |
setMaxIterations(int maxIterations)
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void |
setMaxNumClusters(int maxNumClusters)
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void |
setMinNumClusters(int minNumClusters)
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void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setPrintDebug(boolean printDebug)
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void |
setRestarts(int restarts)
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java.lang.String |
toString()
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Methods inherited from class weka.clusterers.RandomizableClusterer |
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getSeed, seedTipText, setSeed |
Methods inherited from class weka.clusterers.AbstractClusterer |
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forName, makeCopies, makeCopy, runClusterer |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public CascadeSimpleKMeans()
Method Detail |
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public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String globalInfo()
public void buildClusterer(Instances data) throws java.lang.Exception
buildClusterer
in interface Clusterer
buildClusterer
in class AbstractClusterer
java.lang.Exception
public int clusterInstance(Instance instance) throws java.lang.Exception
clusterInstance
in interface Clusterer
clusterInstance
in class AbstractClusterer
java.lang.Exception
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in interface Clusterer
distributionForInstance
in class AbstractClusterer
java.lang.Exception
public int numberOfClusters() throws java.lang.Exception
numberOfClusters
in interface Clusterer
numberOfClusters
in class AbstractClusterer
java.lang.Exception
public Capabilities getCapabilities()
getCapabilities
in interface Clusterer
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class AbstractClusterer
public java.lang.String minNumClustersTipText()
public int getMinNumClusters()
public void setMinNumClusters(int minNumClusters)
public java.lang.String maxNumClustersTipText()
public int getMaxNumClusters()
public void setMaxNumClusters(int maxNumClusters)
public java.lang.String restartsTipText()
public int getRestarts()
public void setRestarts(int restarts)
public java.lang.String printDebugTipText()
public boolean isPrintDebug()
public void setPrintDebug(boolean printDebug)
public java.lang.String distanceFunctionTipText()
public DistanceFunction getDistanceFunction()
public void setDistanceFunction(DistanceFunction distanceFunction)
public java.lang.String maxIterationsTipText()
public int getMaxIterations()
public void setMaxIterations(int maxIterations)
public java.lang.String manuallySelectNumClustersTipText()
public boolean isManuallySelectNumClusters()
public void setManuallySelectNumClusters(boolean manuallySelectNumClusters)
public java.lang.String initializeUsingKMeansPlusPlusMethodTipText()
public void setInitializeUsingKMeansPlusPlusMethod(boolean k)
k
- true if the k-means++ method is to be used to select
initial cluster centers.public boolean getInitializeUsingKMeansPlusPlusMethod()
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableClusterer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-N <num> number of clusters. (default 2).
-P Initialize using the k-means++ method.
-V Display std. deviations for centroids.
-M Replace missing values with mean/mode.
-A <classname and options> Distance function to use. (default: weka.core.EuclideanDistance)
-I <num> Maximum number of iterations.
-O Preserve order of instances.
-fast Enables faster distance calculations, using cut-off values. Disables the calculation/output of squared errors/distances.
-S <num> Random number seed. (default 10)
setOptions
in interface OptionHandler
setOptions
in class RandomizableClusterer
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class RandomizableClusterer
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class AbstractClusterer
public static void main(java.lang.String[] args)
args
- use -h to list all parameters
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