weka.clusterers

## Class Canopy

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

- All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Clusterer, NumberOfClustersRequestable, UpdateableClusterer, CapabilitiesHandler, CapabilitiesIgnorer, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler

      public class Canopy
      extends RandomizableClusterer
      implements UpdateableClusterer, NumberOfClustersRequestable, OptionHandler, TechnicalInformationHandler

  Cluster data using the capopy clustering algorithm, which requires just one pass over the data. Can run in eitherbatch or incremental mode. Results are generally not as good when running incrementally as the min/max for each numeric attribute is not known in advance. Has a heuristic (based on attribute std. deviations), that can be used in batch mode, for setting the T2 distance. The T2 distance determines how many canopies (clusters) are formed. When the user specifies a specific number (N) of clusters to generate, the algorithm will return the top N canopies (as determined by T2 density) when N < number of canopies (this applies to both batch and incremental learning); when N > number of canopies, the difference is made up by selecting training instances randomly (this can only be done when batch training). For more information see:
  A. McCallum, K. Nigam, L.H. Ungar: Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching. In: Proceedings of the sixth ACM SIGKDD internation conference on knowledge discovery and data mining ACM-SIAM symposium on Discrete algorithms, 169-178, 2000.

  BibTeX:

       @inproceedings{McCallum2000,
          author = {A. McCallum and K. Nigam and L.H. Ungar},
          booktitle = {Proceedings of the sixth ACM SIGKDD internation conference on knowledge discovery and data mining ACM-SIAM symposium on Discrete algorithms},
          pages = {169-178},
          title = {Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching},
          year = {2000}
       }

  Valid options are:

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

       -max-candidates <num>
        Maximum number of candidate canopies to retain in memory
        at any one time. 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.
        (default = every 10,000 training instances)

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

       -t2
        The T2 distance to use. Values < 0 indicate that
        a heuristic based on attribute std. deviation should be used to set this.
        Note that this heuristic can only be used when batch training
        (default = -1.0)

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

       -M
        Don't replace missing values with mean/mode when running in batch mode.

       -S <num>
        Random number seed.
        (default 1)

       -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: 11012 $
  Author:
    Mark Hall (mhall{[at]}pentaho{[dot]}com)
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static double` | `DEFAULT_T1`
    `static double` | `DEFAULT_T2`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `Canopy()`

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `static Canopy` | `aggregateCanopies(java.util.List<Canopy> canopies, double aggregationT1, double aggregationT2, NormalizableDistance finalDistanceFunction, Filter missingValuesReplacer, int finalNumCanopies)` Aggregate the canopies from a list of Canopy clusterers together into one final model.
    `long[]` | `assignCanopies(Instance inst)` Uses T1 distance to assign canopies to the supplied instance.
    `void` | `buildClusterer(Instances data)` Generates a clusterer.
    `void` | `cleanUp()` Save memory
    `double[]` | `distributionForInstance(Instance instance)` Predicts the cluster memberships for a given instance.
    `java.lang.String` | `dontReplaceMissingValuesTipText()` Returns the tip text for this property.
    `double` | `getActualT1()` Get the actual value of T1 (which may be different from the initial value if the heuristic is used)
    `double` | `getActualT2()` Get the actual value of T2 (which may be different from the initial value if the heuristic is used)
    `Instances` | `getCanopies()` Get the canopies (cluster centers).
    `Capabilities` | `getCapabilities()` Returns default capabilities of the clusterer.
    `java.util.List<long[]>` | `getClusterCanopyAssignments()` Get the canopies that each canopy (cluster center) is within T1 distance of
    `boolean` | `getDontReplaceMissingValues()` Gets whether missing values are to be replaced.
    `int` | `getMaxNumCandidateCanopiesToHoldInMemory()` Get the maximum number of candidate canopies to retain in memory during training.
    `double` | `getMinimumCanopyDensity()` Get the minimum T2-based density below which a canopy will be pruned during periodic pruning.
    `int` | `getNumClusters()` Get the number of clusters to generate
    `java.lang.String[]` | `getOptions()` Gets the current settings of Canopy.
    `int` | `getPeriodicPruningRate()` Get the how often to prune low density canopies during training
    `double` | `getT1()` Get the T1 distance.
    `double` | `getT2()` Get the T2 distance to use.
    `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.
    `java.lang.String` | `globalInfo()` Returns a string describing this clusterer.
    `void` | `initializeDistanceFunction(Instances init)` Initialize the distance function (i.e set min/max values for numeric attributes) with the supplied instances.
    `java.util.Enumeration<Option>` | `listOptions()` Returns an enumeration describing the available options.
    `static void` | `main(java.lang.String[] args)`
    `java.lang.String` | `maxNumCandidateCanopiesToHoldInMemory()` Returns the tip text for this property.
    `java.lang.String` | `minimumCanopyDensityTipText()` Returns the tip text for this property.
    `static boolean` | `nonEmptyCanopySetIntersection(long[] first, long[] second)` Tests if two sets of canopies have a non-empty intersection
    `int` | `numberOfClusters()` Returns the number of clusters.
    `java.lang.String` | `numClustersTipText()` Returns the tip text for this property.
    `java.lang.String` | `periodicPruningRateTipText()` Returns the tip text for this property.
    `static java.lang.String` | `printCanopyAssignments(Instances dataPoints, java.util.List<long[]> canopyAssignments)` Print the supplied instances and their canopies
    `static java.lang.String` | `printSingleAssignment(long[] assignments)`
    `void` | `setCanopies(Instances canopies)` Set the canopies to use (replaces any learned by this clusterer already)
    `void` | `setClusterCanopyAssignments(java.util.List<long[]> clusterCanopies)` Set the canopies that each canopy (cluster center) is within T1 distance of
    `void` | `setDontReplaceMissingValues(boolean r)` Sets whether missing values are to be replaced.
    `void` | `setMaxNumCandidateCanopiesToHoldInMemory(int max)` Set the maximum number of candidate canopies to retain in memory during training.
    `void` | `setMinimumCanopyDensity(double dens)` Set the minimum T2-based density below which a canopy will be pruned during periodic pruning.
    `void` | `setMissingValuesReplacer(Filter missingReplacer)` Set a ready-to-use missing values replacement filter
    `void` | `setNumClusters(int numClusters)` Set the number of clusters to generate
    `void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
    `void` | `setPeriodicPruningRate(int p)` Set the how often to prune low density canopies during training
    `void` | `setT1(double t1)` Set the T1 distance.
    `void` | `setT2(double t2)` Set the T2 distance to use.
    `java.lang.String` | `t1TipText()` Tip text for this property
    `java.lang.String` | `t2TipText()` Tip text for this property
    `java.lang.String` | `toString()`
    `java.lang.String` | `toString(boolean header)` Return a textual description of this clusterer
    `void` | `updateClusterer(Instance newInstance)` Adds an instance to the clusterer.
    `void` | `updateFinished()` Signals the end of the updating.

    - ### Methods inherited from class weka.clusterers.RandomizableClusterer

      `getSeed, seedTipText, setSeed`

    - ### Methods inherited from class weka.clusterers.AbstractClusterer

      `clusterInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getRevision, makeCopies, makeCopy, runClusterer, setDebug, setDoNotCheckCapabilities`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### Field Detail

    - #### DEFAULT_T2

          public static final double DEFAULT_T2

      See Also:
        Constant Field Values

    - #### DEFAULT_T1

          public static final double DEFAULT_T1

      See Also:
        Constant Field Values

  - ### Constructor Detail

    - #### Canopy

          public Canopy()

  - ### Method Detail

    - #### globalInfo

          public java.lang.String globalInfo()

      Returns a string describing this clusterer.
      Returns:
        a description of the evaluator suitable for displaying in the explorer/experimenter gui

    - #### 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

    - #### 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`

    - #### listOptions

          public java.util.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.

    - #### setOptions

          public void setOptions(java.lang.String[] options)
                          throws java.lang.Exception

      Parses a given list of options.

      Valid options are:

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

           -max-candidates <num>
            Maximum number of candidate canopies to retain in memory
            at any one time. 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.
            (default = every 10,000 training instances)

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

           -t2
            The T2 distance to use. Values < 0 indicate that
            a heuristic based on attribute std. deviation should be used to set this.
            Note that this heuristic can only be used when batch training
            (default = -1.0)

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

           -M
            Don't replace missing values with mean/mode when running in batch mode.

           -S <num>
            Random number seed.
            (default 1)

           -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
      Throws:
        `java.lang.Exception` - if an option is not supported

    - #### getOptions

          public java.lang.String[] getOptions()

      Gets the current settings of Canopy.
      Specified by:
        `getOptions` in interface `OptionHandler`
      Overrides:
        `getOptions` in class `RandomizableClusterer`
      Returns:
        an array of strings suitable for passing to setOptions()

    - #### nonEmptyCanopySetIntersection

          public static boolean nonEmptyCanopySetIntersection(long[] first,
                                              long[] second)
                                                       throws java.lang.Exception

      Tests if two sets of canopies have a non-empty intersection
      Parameters:
        `first` - the first canopy set
        `second` - the second canopy set
      Returns:
        true if the intersection is non-empty
      Throws:
        `java.lang.Exception` - if a problem occurs

    - #### assignCanopies

          public long[] assignCanopies(Instance inst)
                                throws java.lang.Exception

      Uses T1 distance to assign canopies to the supplied instance. If the instance does not fall within T1 distance of any canopies then the instance has the closest canopy assigned to it.
      Parameters:
        `inst` - the instance to find covering canopies for
      Returns:
        a set of canopies that contain this instance according to T1 distance
      Throws:
        `java.lang.Exception` - if a problem occurs

    - #### updateClusterer

          public void updateClusterer(Instance newInstance)
                               throws java.lang.Exception

      Description copied from interface: `UpdateableClusterer`
      Adds an instance to the clusterer.
      Specified by:
        `updateClusterer` in interface `UpdateableClusterer`
      Parameters:
        `newInstance` - the instance to be added
      Throws:
        `java.lang.Exception` - if something goes wrong

    - #### distributionForInstance

          public double[] distributionForInstance(Instance instance)
                                           throws java.lang.Exception

      Description copied from class: `AbstractClusterer`
      Predicts the cluster memberships for a given instance. Either this or clusterInstance() needs to be implemented by subclasses.
      Specified by:
        `distributionForInstance` in interface `Clusterer`
      Overrides:
        `distributionForInstance` in class `AbstractClusterer`
      Parameters:
        `instance` - the instance to be assigned a cluster.
      Returns:
        an array containing the estimated membership probabilities of the test instance in each cluster (this should sum to at most 1)
      Throws:
        `java.lang.Exception` - if distribution could not be computed successfully

    - #### updateFinished

          public void updateFinished()

      Description copied from interface: `UpdateableClusterer`
      Signals the end of the updating.
      Specified by:
        `updateFinished` in interface `UpdateableClusterer`

    - #### initializeDistanceFunction

          public void initializeDistanceFunction(Instances init)
                                          throws java.lang.Exception

      Initialize the distance function (i.e set min/max values for numeric attributes) with the supplied instances.
      Parameters:
        `init` - the instances to initialize with
      Throws:
        `java.lang.Exception` - if a problem occurs

    - #### buildClusterer

          public void buildClusterer(Instances data)
                              throws java.lang.Exception

      Description copied from class: `AbstractClusterer`
      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:
        `java.lang.Exception` - if the clusterer has not been generated successfully

    - #### numberOfClusters

          public int numberOfClusters()
                               throws java.lang.Exception

      Description copied from class: `AbstractClusterer`
      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:
        `java.lang.Exception` - if number of clusters could not be returned successfully

    - #### setMissingValuesReplacer

          public void setMissingValuesReplacer(Filter missingReplacer)

      Set a ready-to-use missing values replacement filter
      Parameters:
        `missingReplacer` - the missing values replacement filter to use

    - #### getCanopies

          public Instances getCanopies()

      Get the canopies (cluster centers).
      Returns:
        the canopies

    - #### setCanopies

          public void setCanopies(Instances canopies)

      Set the canopies to use (replaces any learned by this clusterer already)
      Parameters:
        `canopies` - the canopies to use

    - #### getClusterCanopyAssignments

          public java.util.List<long[]> getClusterCanopyAssignments()

      Get the canopies that each canopy (cluster center) is within T1 distance of
      Returns:
        a list of canopies for each cluster center

    - #### setClusterCanopyAssignments

          public void setClusterCanopyAssignments(java.util.List<long[]> clusterCanopies)

      Set the canopies that each canopy (cluster center) is within T1 distance of
      Parameters:
        `clusterCanopies` - the list canopies for each cluster center

    - #### getActualT2

          public double getActualT2()

      Get the actual value of T2 (which may be different from the initial value if the heuristic is used)
      Returns:
        the actual value of T2

    - #### getActualT1

          public double getActualT1()

      Get the actual value of T1 (which may be different from the initial value if the heuristic is used)
      Returns:
        the actual value of T1

    - #### t1TipText

          public java.lang.String t1TipText()

      Tip text for this property
      Returns:
        the tip text for this property

    - #### setT1

          public void setT1(double t1)

      Set the T1 distance. Values < 0 are taken as a positive multiplier for the T2 distance - e.g. T1_actual = Math.abs(t1) * t2;
      Parameters:
        `t1` - the T1 distance to use

    - #### getT1

          public double getT1()

      Get the T1 distance. Values < 0 are taken as a positive multiplier for the T2 distance - e.g. T1_actual = Math.abs(t1) * t2;
      Returns:
        the T1 distance to use

    - #### t2TipText

          public java.lang.String t2TipText()

      Tip text for this property
      Returns:
        the tip text for this property

    - #### setT2

          public void setT2(double t2)

      Set the T2 distance to use. Values < 0 indicate that a heuristic based on attribute standard deviation should be used to set this (note that the heuristic is only applicable when batch training).
      Parameters:
        `t2` - the T2 distance to use

    - #### getT2

          public double getT2()

      Get the T2 distance to use. Values < 0 indicate that a heuristic based on attribute standard deviation should be used to set this (note that the heuristic is only applicable when batch training).
      Returns:
        the T2 distance to use

    - #### numClustersTipText

          public java.lang.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 numClusters)
                              throws java.lang.Exception

      Description copied from interface: `NumberOfClustersRequestable`
      Set the number of clusters to generate
      Specified by:
        `setNumClusters` in interface `NumberOfClustersRequestable`
      Parameters:
        `numClusters` - the number of clusters to generate
      Throws:
        `java.lang.Exception` - if the requested number of clusters in inapropriate

    - #### getNumClusters

          public int getNumClusters()

      Get the number of clusters to generate
      Returns:
        the number of clusters to generate

    - #### periodicPruningRateTipText

          public java.lang.String periodicPruningRateTipText()

      Returns the tip text for this property.
      Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

    - #### setPeriodicPruningRate

          public void setPeriodicPruningRate(int p)

      Set the how often to prune low density canopies during training
      Parameters:
        `p` - how often (every p instances) to prune low density canopies

    - #### getPeriodicPruningRate

          public int getPeriodicPruningRate()

      Get the how often to prune low density canopies during training
      Returns:
        how often (every p instances) to prune low density canopies

    - #### minimumCanopyDensityTipText

          public java.lang.String minimumCanopyDensityTipText()

      Returns the tip text for this property.
      Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

    - #### setMinimumCanopyDensity

          public void setMinimumCanopyDensity(double dens)

      Set the minimum T2-based density below which a canopy will be pruned during periodic pruning.
      Parameters:
        `dens` - the minimum canopy density

    - #### getMinimumCanopyDensity

          public double getMinimumCanopyDensity()

      Get the minimum T2-based density below which a canopy will be pruned during periodic pruning.
      Returns:
        the minimum canopy density

    - #### maxNumCandidateCanopiesToHoldInMemory

          public java.lang.String maxNumCandidateCanopiesToHoldInMemory()

      Returns the tip text for this property.
      Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

    - #### setMaxNumCandidateCanopiesToHoldInMemory

          public void setMaxNumCandidateCanopiesToHoldInMemory(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

    - #### getMaxNumCandidateCanopiesToHoldInMemory

          public int getMaxNumCandidateCanopiesToHoldInMemory()

      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

    - #### dontReplaceMissingValuesTipText

          public java.lang.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

    - #### printSingleAssignment

          public static java.lang.String printSingleAssignment(long[] assignments)

    - #### printCanopyAssignments

          public static java.lang.String printCanopyAssignments(Instances dataPoints,
                                                java.util.List<long[]> canopyAssignments)

      Print the supplied instances and their canopies
      Parameters:
        `dataPoints` - the instances to print
        `canopyAssignments` - the canopy assignments, one assignment array for each instance
      Returns:
        a string containing the printed assignments

    - #### toString

          public java.lang.String toString(boolean header)

      Return a textual description of this clusterer
      Parameters:
        `header` - true if the header should be printed
      Returns:
        a string describing the result of the clustering

    - #### toString

          public java.lang.String toString()

      Overrides:
        `toString` in class `java.lang.Object`

    - #### cleanUp

          public void cleanUp()

      Save memory

    - #### aggregateCanopies

          public static Canopy aggregateCanopies(java.util.List<Canopy> canopies,
                                 double aggregationT1,
                                 double aggregationT2,
                                 NormalizableDistance finalDistanceFunction,
                                 Filter missingValuesReplacer,
                                 int finalNumCanopies)

      Aggregate the canopies from a list of Canopy clusterers together into one final model.
      Parameters:
        `canopies` - the list of Canopy clusterers to aggregate
        `aggregationT1` - the T1 distance to use for the aggregated classifier
        `aggregationT2` - the T2 distance to use when aggregating canopies
        `finalDistanceFunction` - the distance function to use with the final Canopy clusterer
        `missingValuesReplacer` - the missing value replacement filter to use with the final clusterer (can be null for no missing value replacement)
        `finalNumCanopies` - the final number of canopies
      Returns:
        a Canopy clusterer that aggregates all the canopies

    - #### main

          public static void main(java.lang.String[] args)