SimpleKMeans (weka-dev 3.7.11 API)

java.lang.Object
- weka.clusterers.AbstractClusterer
- - weka.clusterers.RandomizableClusterer
  - - weka.clusterers.SimpleKMeans

All Implemented Interfaces:: Serializable, Cloneable, Clusterer, NumberOfClustersRequestable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class SimpleKMeans
extends RandomizableClusterer
implements NumberOfClustersRequestable, WeightedInstancesHandler, TechnicalInformationHandler

Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the component-wise median rather than mean. For more information see:

D. Arthur, S. Vassilvitskii: k-means++: the advantages of carefull seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.

BibTeX:

 @inproceedings{Arthur2007,
    author = {D. Arthur and S. Vassilvitskii},
    booktitle = {Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
    pages = {1027-1035},
    title = {k-means++: the advantages of carefull seeding},
    year = {2007}
 }

Valid options are:

 -N <num>
  Number of clusters.
  (default 2).

 -init
  Initialization method to use.
  0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first.
  (default = 0)

 -C
  Use canopies to reduce the number of distance calculations.

 -max-candidates <num>
  Maximum number of candidate canopies to retain in memory
  at any one time when using canopy clustering.
  T2 distance plus, data characteristics,
  will determine how many candidate canopies are formed before
  periodic and final pruning are performed, which might result
  in exceess memory consumption. This setting avoids large numbers
  of candidate canopies consuming memory. (default = 100)

 -periodic-pruning <num>
  How often to prune low density canopies when using canopy clustering. 
  (default = every 10,000 training instances)

 -min-density
  Minimum canopy density, when using canopy clustering, below which
   a canopy will be pruned during periodic pruning. (default = 2 instances)

 -t2
  The T2 distance to use when using canopy clustering. Values < 0 indicate that
  a heuristic based on attribute std. deviation should be used to set this.
  (default = -1.0)

 -t1
  The T1 distance to use when using canopy clustering. A value < 0 is taken as a
  positive multiplier for T2. (default = -1.5)

 -V
  Display std. deviations for centroids.

 -M
  Don't 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.

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -S <num>
  Random number seed.
  (default 10)

 -output-debug-info
  If set, clusterer is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, clusterer capabilities are not checked before clusterer is built
  (use with caution).

Version:: $Revision: 10605 $
Author:: Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:: RandomizableClusterer, Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`CANOPY`
`static int`	`FARTHEST_FIRST`
`static int`	`KMEANS_PLUS_PLUS`
`static int`	`RANDOM`
`static Tag[]`	`TAGS_SELECTION` Initialization methods

Constructor Summary

Constructors
Constructor and Description

SimpleKMeans()
the default constructor.

Constructors
Constructor and Description
`SimpleKMeans()` the default constructor.

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`buildClusterer(Instances data)` Generates a clusterer.
`String`	`canopyMaxNumCanopiesToHoldInMemoryTipText()` Returns the tip text for this property.
`String`	`canopyMinimumCanopyDensityTipText()` Returns the tip text for this property.
`String`	`canopyPeriodicPruningRateTipText()` Returns the tip text for this property.
`String`	`canopyT1TipText()` Tip text for this property
`String`	`canopyT2TipText()` Tip text for this property
`int`	`clusterInstance(Instance instance)` Classifies a given instance.
`String`	`displayStdDevsTipText()` Returns the tip text for this property.
`String`	`distanceFunctionTipText()` Returns the tip text for this property.
`String`	`dontReplaceMissingValuesTipText()` Returns the tip text for this property.
`String`	`fastDistanceCalcTipText()` Returns the tip text for this property.
`int[]`	`getAssignments()` Gets the assignments for each instance.
`int`	`getCanopyMaxNumCanopiesToHoldInMemory()` Get the maximum number of candidate canopies to retain in memory during training.
`double`	`getCanopyMinimumCanopyDensity()` Get the minimum T2-based density below which a canopy will be pruned during periodic pruning.
`int`	`getCanopyPeriodicPruningRate()` Get the how often to prune low density canopies during training (if using canopy clustering)
`double`	`getCanopyT1()` Get the t1 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
`double`	`getCanopyT2()` Get the t2 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
`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.
`DistanceFunction`	`getDistanceFunction()` returns the distance function currently in use.
`boolean`	`getDontReplaceMissingValues()` Gets whether missing values are to be replaced.
`boolean`	`getFastDistanceCalc()` Gets whether to use faster distance calculation.
`SelectedTag`	`getInitializationMethod()` Get the initialization method to use
`int`	`getMaxIterations()` gets the number of maximum iterations to be executed.
`int`	`getNumClusters()` gets the number of clusters to generate.
`int`	`getNumExecutionSlots()` Get the degree of parallelism to use.
`String[]`	`getOptions()` Gets the current settings of SimpleKMeans.
`boolean`	`getPreserveInstancesOrder()` Gets whether order of instances must be preserved.
`boolean`	`getReduceNumberOfDistanceCalcsViaCanopies()` Get whether to use canopies to reduce the number of distance computations required
`String`	`getRevision()` Returns the revision string.
`double`	`getSquaredError()` Gets the squared error for all clusters.
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`String`	`globalInfo()` Returns a string describing this clusterer.
`String`	`initializationMethodTipText()` Returns the tip text for this property.
`Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] args)` Main method for executing this class.
`String`	`maxIterationsTipText()` Returns the tip text for this property.
`int`	`numberOfClusters()` Returns the number of clusters.
`String`	`numClustersTipText()` Returns the tip text for this property.
`String`	`numExecutionSlotsTipText()` Returns the tip text for this property
`String`	`preserveInstancesOrderTipText()` Returns the tip text for this property.
`String`	`reduceNumberOfDistanceCalcsViaCanopiesTipText()` Returns the tip text for this property.
`void`	`setCanopyMaxNumCanopiesToHoldInMemory(int max)` Set the maximum number of candidate canopies to retain in memory during training.
`void`	`setCanopyMinimumCanopyDensity(double dens)` Set the minimum T2-based density below which a canopy will be pruned during periodic pruning.
`void`	`setCanopyPeriodicPruningRate(int p)` Set the how often to prune low density canopies during training (if using canopy clustering)
`void`	`setCanopyT1(double t1)` Set the t1 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
`void`	`setCanopyT2(double t2)` Set the t2 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
`void`	`setDisplayStdDevs(boolean stdD)` Sets whether standard deviations and nominal count.
`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`	`setFastDistanceCalc(boolean value)` Sets whether to use faster distance calculation.
`void`	`setInitializationMethod(SelectedTag method)` Set the initialization method to use
`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`	`setNumExecutionSlots(int slots)` Set the degree of parallelism to use.
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setPreserveInstancesOrder(boolean r)` Sets whether order of instances must be preserved.
`void`	`setReduceNumberOfDistanceCalcsViaCanopies(boolean c)` Set whether to use canopies to reduce the number of distance computations required
`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
debugTipText, distributionForInstance, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, runClusterer, setDebug, setDoNotCheckCapabilities

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

- Field Detail
  - RANDOM
```
public static final int RANDOM
```
    See Also:
    Constant Field Values
  - KMEANS_PLUS_PLUS
```
public static final int KMEANS_PLUS_PLUS
```
    See Also:
    Constant Field Values
  - CANOPY
```
public static final int CANOPY
```
    See Also:
    Constant Field Values
  - FARTHEST_FIRST
```
public static final int FARTHEST_FIRST
```
    See Also:
    Constant Field Values
  - TAGS_SELECTION
```
public static final Tag[] TAGS_SELECTION
```
    Initialization methods
- Constructor Detail
  - SimpleKMeans
```
public SimpleKMeans()
```
    the default constructor.
- Method Detail
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Description copied from interface: TechnicalInformationHandler
    
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    the technical information about this class
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing this clusterer.
    
    Returns:
    a description of the evaluator suitable for displaying in the explorer/experimenter gui
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the clusterer.
    
    Specified by:
    
    getCapabilities in interface Clusterer
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClusterer
    
    Returns:
    the capabilities of this clusterer
    See Also:
    Capabilities
  - buildClusterer
```
public void buildClusterer(Instances data)
                    throws Exception
```
    Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.
    
    Specified by:
    
    buildClusterer in interface Clusterer
    
    Specified by:
    
    buildClusterer in class AbstractClusterer
    
    Parameters:
    data - set of instances serving as training data
    
    Throws:
    
    Exception - if the clusterer has not been generated successfully
  - clusterInstance
```
public int clusterInstance(Instance instance)
                    throws Exception
```
    Classifies a given instance.
    
    Specified by:
    
    clusterInstance in interface Clusterer
    
    Overrides:
    
    clusterInstance in class AbstractClusterer
    
    Parameters:
    instance - the instance to be assigned to a cluster
    
    Returns:
    the number of the assigned cluster as an interger if the class is enumerated, otherwise the predicted value
    
    Throws:
    
    Exception - if instance could not be classified successfully
  - numberOfClusters
```
public int numberOfClusters()
                     throws Exception
```
    Returns the number of clusters.
    
    Specified by:
    
    numberOfClusters in interface Clusterer
    
    Specified by:
    
    numberOfClusters in class AbstractClusterer
    
    Returns:
    the number of clusters generated for a training dataset.
    
    Throws:
    
    Exception - if number of clusters could not be returned successfully
  - listOptions
```
public Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableClusterer
    
    Returns:
    an enumeration of all the available options.
  - numClustersTipText
```
public String numClustersTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumClusters
```
public void setNumClusters(int n)
                    throws Exception
```
    set the number of clusters to generate.
    
    Specified by:
    
    setNumClusters in interface NumberOfClustersRequestable
    
    Parameters:
    n - the number of clusters to generate
    
    Throws:
    
    Exception - if number of clusters is negative
  - getNumClusters
```
public int getNumClusters()
```
    gets the number of clusters to generate.
    
    Returns:
    the number of clusters to generate
  - initializationMethodTipText
```
public String initializationMethodTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setInitializationMethod
```
public void setInitializationMethod(SelectedTag method)
```
    Set the initialization method to use
    
    Parameters:
    method - the initialization method to use
  - getInitializationMethod
```
public SelectedTag getInitializationMethod()
```
    Get the initialization method to use
    
    Returns:
    method the initialization method to use
  - reduceNumberOfDistanceCalcsViaCanopiesTipText
```
public String reduceNumberOfDistanceCalcsViaCanopiesTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setReduceNumberOfDistanceCalcsViaCanopies
```
public void setReduceNumberOfDistanceCalcsViaCanopies(boolean c)
```
    Set whether to use canopies to reduce the number of distance computations required
    
    Parameters:
    c - true if canopies are to be used to reduce the number of distance computations
  - getReduceNumberOfDistanceCalcsViaCanopies
```
public boolean getReduceNumberOfDistanceCalcsViaCanopies()
```
    Get whether to use canopies to reduce the number of distance computations required
    
    Returns:
    true if canopies are to be used to reduce the number of distance computations
  - canopyPeriodicPruningRateTipText
```
public String canopyPeriodicPruningRateTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCanopyPeriodicPruningRate
```
public void setCanopyPeriodicPruningRate(int p)
```
    Set the how often to prune low density canopies during training (if using canopy clustering)
    
    Parameters:
    p - how often (every p instances) to prune low density canopies
  - getCanopyPeriodicPruningRate
```
public int getCanopyPeriodicPruningRate()
```
    Get the how often to prune low density canopies during training (if using canopy clustering)
    
    Returns:
    how often (every p instances) to prune low density canopies
  - canopyMinimumCanopyDensityTipText
```
public String canopyMinimumCanopyDensityTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCanopyMinimumCanopyDensity
```
public void setCanopyMinimumCanopyDensity(double dens)
```
    Set the minimum T2-based density below which a canopy will be pruned during periodic pruning.
    
    Parameters:
    dens - the minimum canopy density
  - getCanopyMinimumCanopyDensity
```
public double getCanopyMinimumCanopyDensity()
```
    Get the minimum T2-based density below which a canopy will be pruned during periodic pruning.
    
    Returns:
    the minimum canopy density
  - canopyMaxNumCanopiesToHoldInMemoryTipText
```
public String canopyMaxNumCanopiesToHoldInMemoryTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCanopyMaxNumCanopiesToHoldInMemory
```
public void setCanopyMaxNumCanopiesToHoldInMemory(int max)
```
    Set the maximum number of candidate canopies to retain in memory during training. T2 distance and data characteristics determine how many candidate canopies are formed before periodic and final pruning are performed. There may not be enough memory available if T2 is set too low.
    
    Parameters:
    max - the maximum number of candidate canopies to retain in memory during training
  - getCanopyMaxNumCanopiesToHoldInMemory
```
public int getCanopyMaxNumCanopiesToHoldInMemory()
```
    Get the maximum number of candidate canopies to retain in memory during training. T2 distance and data characteristics determine how many candidate canopies are formed before periodic and final pruning are performed. There may not be enough memory available if T2 is set too low.
    
    Returns:
    the maximum number of candidate canopies to retain in memory during training
  - canopyT2TipText
```
public String canopyT2TipText()
```
    Tip text for this property
    
    Returns:
    the tip text for this property
  - setCanopyT2
```
public void setCanopyT2(double t2)
```
    Set the t2 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
    
    Parameters:
    t2 - the t2 radius to use
  - getCanopyT2
```
public double getCanopyT2()
```
    Get the t2 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
    
    Returns:
    the t2 radius to use
  - canopyT1TipText
```
public String canopyT1TipText()
```
    Tip text for this property
    
    Returns:
    the tip text for this property
  - setCanopyT1
```
public void setCanopyT1(double t1)
```
    Set the t1 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
    
    Parameters:
    t1 - the t1 radius to use
  - getCanopyT1
```
public double getCanopyT1()
```
    Get the t1 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
    
    Returns:
    the t1 radius to use
  - maxIterationsTipText
```
public String maxIterationsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMaxIterations
```
public void setMaxIterations(int n)
                      throws Exception
```
    set the maximum number of iterations to be executed.
    
    Parameters:
    n - the maximum number of iterations
    
    Throws:
    
    Exception - if maximum number of iteration is smaller than 1
  - getMaxIterations
```
public int getMaxIterations()
```
    gets the number of maximum iterations to be executed.
    
    Returns:
    the number of clusters to generate
  - displayStdDevsTipText
```
public String displayStdDevsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDisplayStdDevs
```
public void setDisplayStdDevs(boolean stdD)
```
    Sets whether standard deviations and nominal count. Should be displayed in the clustering output.
    
    Parameters:
    stdD - true if std. devs and counts should be displayed
  - getDisplayStdDevs
```
public boolean getDisplayStdDevs()
```
    Gets whether standard deviations and nominal count. Should be displayed in the clustering output.
    
    Returns:
    true if std. devs and counts should be displayed
  - dontReplaceMissingValuesTipText
```
public String dontReplaceMissingValuesTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDontReplaceMissingValues
```
public void setDontReplaceMissingValues(boolean r)
```
    Sets whether missing values are to be replaced.
    
    Parameters:
    r - true if missing values are to be replaced
  - getDontReplaceMissingValues
```
public boolean getDontReplaceMissingValues()
```
    Gets whether missing values are to be replaced.
    
    Returns:
    true if missing values are to be replaced
  - distanceFunctionTipText
```
public String distanceFunctionTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getDistanceFunction
```
public DistanceFunction getDistanceFunction()
```
    returns the distance function currently in use.
    
    Returns:
    the distance function
  - setDistanceFunction
```
public void setDistanceFunction(DistanceFunction df)
                         throws Exception
```
    sets the distance function to use for instance comparison.
    
    Parameters:
    df - the new distance function to use
    
    Throws:
    
    Exception - if instances cannot be processed
  - preserveInstancesOrderTipText
```
public String preserveInstancesOrderTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPreserveInstancesOrder
```
public void setPreserveInstancesOrder(boolean r)
```
    Sets whether order of instances must be preserved.
    
    Parameters:
    r - true if missing values are to be replaced
  - getPreserveInstancesOrder
```
public boolean getPreserveInstancesOrder()
```
    Gets whether order of instances must be preserved.
    
    Returns:
    true if missing values are to be replaced
  - fastDistanceCalcTipText
```
public String fastDistanceCalcTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setFastDistanceCalc
```
public void setFastDistanceCalc(boolean value)
```
    Sets whether to use faster distance calculation.
    
    Parameters:
    value - true if faster calculation to be used
  - getFastDistanceCalc
```
public boolean getFastDistanceCalc()
```
    Gets whether to use faster distance calculation.
    
    Returns:
    true if faster calculation is used
  - numExecutionSlotsTipText
```
public String numExecutionSlotsTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumExecutionSlots
```
public void setNumExecutionSlots(int slots)
```
    Set the degree of parallelism to use.
    
    Parameters:
    slots - the number of tasks to run in parallel when computing the nearest neighbors and evaluating different values of k between the lower and upper bounds
  - getNumExecutionSlots
```
public int getNumExecutionSlots()
```
    Get the degree of parallelism to use.
    
    Returns:
    the number of tasks to run in parallel when computing the nearest neighbors and evaluating different values of k between the lower and upper bounds
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -N <num>
  Number of clusters.
  (default 2).
```
```
 -init
  Initialization method to use.
  0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first.
  (default = 0)
```
```
 -C
  Use canopies to reduce the number of distance calculations.
```
```
 -max-candidates <num>
  Maximum number of candidate canopies to retain in memory
  at any one time when using canopy clustering.
  T2 distance plus, data characteristics,
  will determine how many candidate canopies are formed before
  periodic and final pruning are performed, which might result
  in exceess memory consumption. This setting avoids large numbers
  of candidate canopies consuming memory. (default = 100)
```
```
 -periodic-pruning <num>
  How often to prune low density canopies when using canopy clustering. 
  (default = every 10,000 training instances)
```
```
 -min-density
  Minimum canopy density, when using canopy clustering, below which
   a canopy will be pruned during periodic pruning. (default = 2 instances)
```
```
 -t2
  The T2 distance to use when using canopy clustering. Values < 0 indicate that
  a heuristic based on attribute std. deviation should be used to set this.
  (default = -1.0)
```
```
 -t1
  The T1 distance to use when using canopy clustering. A value < 0 is taken as a
  positive multiplier for T2. (default = -1.5)
```
```
 -V
  Display std. deviations for centroids.
 
```
```
 -M
  Don't 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.
 
```
```
 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)
```
```
 -S <num>
  Random number seed.
  (default 10)
```
```
 -output-debug-info
  If set, clusterer is run in debug mode and
  may output additional info to the console
```
```
 -do-not-check-capabilities
  If set, clusterer capabilities are not checked before clusterer is built
  (use with caution).
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableClusterer
    
    Parameters:
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of SimpleKMeans.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableClusterer
    
    Returns:
    an array of strings suitable for passing to setOptions()
  - toString
```
public String toString()
```
    return a string describing this clusterer.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    a description of the clusterer as a string
  - getClusterCentroids
```
public Instances getClusterCentroids()
```
    Gets the the cluster centroids.
    
    Returns:
    the cluster centroids
  - getClusterStandardDevs
```
public Instances getClusterStandardDevs()
```
    Gets the standard deviations of the numeric attributes in each cluster.
    
    Returns:
    the standard deviations of the numeric attributes in each cluster
  - getClusterNominalCounts
```
public int[][][] getClusterNominalCounts()
```
    Returns for each cluster the frequency counts for the values of each nominal attribute.
    
    Returns:
    the counts
  - getSquaredError
```
public double getSquaredError()
```
    Gets the squared error for all clusters.
    
    Returns:
    the squared error, NaN if fast distance calculation is used
    See Also:
    m_FastDistanceCalc
  - getClusterSizes
```
public int[] getClusterSizes()
```
    Gets the number of instances in each cluster.
    
    Returns:
    The number of instances in each cluster
  - getAssignments
```
public int[] getAssignments()
                     throws Exception
```
    Gets the assignments for each instance.
    
    Returns:
    Array of indexes of the centroid assigned to each instance
    
    Throws:
    
    Exception - if order of instances wasn't preserved or no assignments were made
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClusterer
    
    Returns:
    the revision
  - main
```
public static void main(String[] args)
```
    Main method for executing this class.
    
    Parameters:
    args - use -h to list all parameters

Class SimpleKMeans

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.clusterers.RandomizableClusterer

Methods inherited from class weka.clusterers.AbstractClusterer

Methods inherited from class java.lang.Object

Field Detail

RANDOM

KMEANS_PLUS_PLUS

CANOPY

FARTHEST_FIRST

TAGS_SELECTION

Constructor Detail

SimpleKMeans

Method Detail

getTechnicalInformation

globalInfo

getCapabilities

buildClusterer

clusterInstance

numberOfClusters

listOptions

numClustersTipText

setNumClusters

getNumClusters

initializationMethodTipText

setInitializationMethod

getInitializationMethod

reduceNumberOfDistanceCalcsViaCanopiesTipText

setReduceNumberOfDistanceCalcsViaCanopies

getReduceNumberOfDistanceCalcsViaCanopies

canopyPeriodicPruningRateTipText

setCanopyPeriodicPruningRate

getCanopyPeriodicPruningRate

canopyMinimumCanopyDensityTipText

setCanopyMinimumCanopyDensity

getCanopyMinimumCanopyDensity

canopyMaxNumCanopiesToHoldInMemoryTipText

setCanopyMaxNumCanopiesToHoldInMemory

getCanopyMaxNumCanopiesToHoldInMemory

canopyT2TipText

setCanopyT2

getCanopyT2

canopyT1TipText

setCanopyT1

getCanopyT1

maxIterationsTipText

setMaxIterations

getMaxIterations

displayStdDevsTipText

setDisplayStdDevs

getDisplayStdDevs

dontReplaceMissingValuesTipText

setDontReplaceMissingValues

getDontReplaceMissingValues

distanceFunctionTipText

getDistanceFunction

setDistanceFunction

preserveInstancesOrderTipText

setPreserveInstancesOrder

getPreserveInstancesOrder

fastDistanceCalcTipText

setFastDistanceCalc

getFastDistanceCalc

numExecutionSlotsTipText

setNumExecutionSlots

getNumExecutionSlots

setOptions

getOptions

toString

getClusterCentroids

getClusterStandardDevs

getClusterNominalCounts

getSquaredError

getClusterSizes

getAssignments

getRevision

main