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java.lang.Objectweka.clusterers.AbstractClusterer
weka.clusterers.RandomizableClusterer
weka.clusterers.SimpleKMeans
public class SimpleKMeans
Cluster data using the k means algorithm
Valid options are:-N <num> number of clusters. (default 2).
-V Display std. deviations for centroids.
-M Replace missing values with mean/mode.
-S <num> Random number seed. (default 10)
-A <classname and options> Distance function to be used for instance comparison (default weka.core.EuclidianDistance)
-I <num> Maximum number of iterations.
-O Preserve order of instances.
RandomizableClusterer,
Serialized Form| Constructor Summary | |
|---|---|
SimpleKMeans()
the default constructor |
|
| Method Summary | |
|---|---|
void |
buildClusterer(Instances data)
Generates a clusterer. |
int |
clusterInstance(Instance instance)
Classifies a given instance. |
java.lang.String |
displayStdDevsTipText()
Returns the tip text for this property |
java.lang.String |
distanceFunctionTipText()
Returns the tip text for this property. |
java.lang.String |
dontReplaceMissingValuesTipText()
Returns the tip text for this property |
int[] |
getAssignments()
Gets the assignments for each instance |
Capabilities |
getCapabilities()
Returns default capabilities of the clusterer. |
Instances |
getClusterCentroids()
Gets the the cluster centroids |
int[][][] |
getClusterNominalCounts()
Returns for each cluster the frequency counts for the values of each nominal attribute |
int[] |
getClusterSizes()
Gets the number of instances in each cluster |
Instances |
getClusterStandardDevs()
Gets the standard deviations of the numeric attributes in each cluster |
boolean |
getDisplayStdDevs()
Gets whether standard deviations and nominal count Should be displayed in the clustering output |
DistanceFunction |
getDistanceFunction()
returns the distance function currently in use. |
boolean |
getDontReplaceMissingValues()
Gets whether missing values are to be replaced |
int |
getMaxIterations()
gets the number of maximum iterations to be executed |
int |
getNumClusters()
gets the number of clusters to generate |
java.lang.String[] |
getOptions()
Gets the current settings of SimpleKMeans |
boolean |
getPreserveInstancesOrder()
Gets whether order of instances must be preserved |
java.lang.String |
getRevision()
Returns the revision string. |
double |
getSquaredError()
Gets the squared error for all clusters |
java.lang.String |
globalInfo()
Returns a string describing this clusterer |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
java.lang.String |
maxIterationsTipText()
Returns the tip text for this property |
int |
numberOfClusters()
Returns the number of clusters. |
java.lang.String |
numClustersTipText()
Returns the tip text for this property |
java.lang.String |
preserveInstancesOrderTipText()
Returns the tip text for this property |
void |
setDisplayStdDevs(boolean stdD)
Sets whether standard deviations and nominal count Should be displayed in the clustering output |
void |
setDistanceFunction(DistanceFunction df)
sets the distance function to use for instance comparison. |
void |
setDontReplaceMissingValues(boolean r)
Sets whether missing values are to be replaced |
void |
setMaxIterations(int n)
set the maximum number of iterations to be executed |
void |
setNumClusters(int n)
set the number of clusters to generate |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setPreserveInstancesOrder(boolean r)
Sets whether order of instances must be preserved |
java.lang.String |
toString()
return a string describing this clusterer |
| Methods inherited from class weka.clusterers.RandomizableClusterer |
|---|
getSeed, seedTipText, setSeed |
| Methods inherited from class weka.clusterers.AbstractClusterer |
|---|
distributionForInstance, forName, makeCopies, makeCopy |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public SimpleKMeans()
| Method Detail |
|---|
public java.lang.String globalInfo()
public Capabilities getCapabilities()
getCapabilities in interface ClusterergetCapabilities in interface CapabilitiesHandlergetCapabilities in class AbstractClustererCapabilities
public void buildClusterer(Instances data)
throws java.lang.Exception
buildClusterer in interface ClustererbuildClusterer in class AbstractClustererdata - set of instances serving as training data
java.lang.Exception - if the clusterer has not been
generated successfully
public int clusterInstance(Instance instance)
throws java.lang.Exception
clusterInstance in interface ClustererclusterInstance in class AbstractClustererinstance - the instance to be assigned to a cluster
java.lang.Exception - if instance could not be classified
successfully
public int numberOfClusters()
throws java.lang.Exception
numberOfClusters in interface ClusterernumberOfClusters in class AbstractClustererjava.lang.Exception - if number of clusters could not be returned
successfullypublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class RandomizableClustererpublic java.lang.String numClustersTipText()
public void setNumClusters(int n)
throws java.lang.Exception
setNumClusters in interface NumberOfClustersRequestablen - the number of clusters to generate
java.lang.Exception - if number of clusters is negativepublic int getNumClusters()
public java.lang.String maxIterationsTipText()
public void setMaxIterations(int n)
throws java.lang.Exception
n - the maximum number of iterations
java.lang.Exception - if maximum number of iteration is smaller than 1public int getMaxIterations()
public java.lang.String displayStdDevsTipText()
public void setDisplayStdDevs(boolean stdD)
stdD - true if std. devs and counts should be
displayedpublic boolean getDisplayStdDevs()
public java.lang.String dontReplaceMissingValuesTipText()
public void setDontReplaceMissingValues(boolean r)
r - true if missing values are to be
replacedpublic boolean getDontReplaceMissingValues()
public java.lang.String distanceFunctionTipText()
public DistanceFunction getDistanceFunction()
public void setDistanceFunction(DistanceFunction df)
throws java.lang.Exception
df - the new distance function to use
java.lang.Exception - if instances cannot be processedpublic java.lang.String preserveInstancesOrderTipText()
public void setPreserveInstancesOrder(boolean r)
r - true if missing values are to be
replacedpublic boolean getPreserveInstancesOrder()
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-N <num> number of clusters. (default 2).
-V Display std. deviations for centroids.
-M Replace missing values with mean/mode.
-S <num> Random number seed. (default 10)
-A <classname and options> Distance function to be used for instance comparison (default weka.core.EuclidianDistance)
-I <num> Maximum number of iterations.
-O Preserve order of instances.
setOptions in interface OptionHandlersetOptions in class RandomizableClustereroptions - 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 OptionHandlergetOptions in class RandomizableClustererpublic java.lang.String toString()
toString in class java.lang.Objectpublic Instances getClusterCentroids()
public Instances getClusterStandardDevs()
public int[][][] getClusterNominalCounts()
public double getSquaredError()
public int[] getClusterSizes()
public int[] getAssignments()
throws java.lang.Exception
java.lang.Exception - if order of instances wasn't preserved or no assignments were madepublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class AbstractClustererpublic static void main(java.lang.String[] argv)
argv - should contain the following arguments: -t training file [-N number of clusters]
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