Package org.apache.spark.ml.clustering

Class BisectingKMeans

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<BisectingKMeansModel>
org.apache.spark.ml.clustering.BisectingKMeans
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, BisectingKMeansParams, Params, HasDistanceMeasure, HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed, HasWeightCol, DefaultParamsWritable, Identifiable, MLWritable, scala.Serializable
public class BisectingKMeans extends Estimator<BisectingKMeansModel> implements BisectingKMeansParams, DefaultParamsWritable
A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark. The algorithm starts from a single cluster that contains all points. Iteratively it finds divisible clusters on the bottom level and bisects each of them using k-means, until there are k leaf clusters in total or no leaf clusters are divisible. The bisecting steps of clusters on the same level are grouped together to increase parallelism. If bisecting all divisible clusters on the bottom level would result more than k leaf clusters, larger clusters get higher priority.

See Also:

  • Constructor Details

    • BisectingKMeans

      public BisectingKMeans(String uid)

    • BisectingKMeans

      public BisectingKMeans()

  • Method Details

    • load

      public static BisectingKMeans load(String path)

    • read

      public static MLReader<T> read()

    • k

      public final IntParam k()
      Description copied from interface: BisectingKMeansParams
      The desired number of leaf clusters. Must be &gt; 1. Default: 4. The actual number could be smaller if there are no divisible leaf clusters.
      Specified by:
      k in interface BisectingKMeansParams
      Returns:
      (undocumented)

    • minDivisibleClusterSize

      public final DoubleParam minDivisibleClusterSize()
      Description copied from interface: BisectingKMeansParams
      The minimum number of points (if greater than or equal to 1.0) or the minimum proportion of points (if less than 1.0) of a divisible cluster (default: 1.0).
      Specified by:
      minDivisibleClusterSize in interface BisectingKMeansParams
      Returns:
      (undocumented)

    • weightCol

      public final Param<String> weightCol()
      Description copied from interface: HasWeightCol
      Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
      Specified by:
      weightCol in interface HasWeightCol
      Returns:
      (undocumented)

    • distanceMeasure

      public final Param<String> distanceMeasure()
      Description copied from interface: HasDistanceMeasure
      Param for The distance measure. Supported options: 'euclidean' and 'cosine'.
      Specified by:
      distanceMeasure in interface HasDistanceMeasure
      Returns:
      (undocumented)

    • predictionCol

      public final Param<String> predictionCol()
      Description copied from interface: HasPredictionCol
      Param for prediction column name.
      Specified by:
      predictionCol in interface HasPredictionCol
      Returns:
      (undocumented)

    • seed

      public final LongParam seed()
      Description copied from interface: HasSeed
      Param for random seed.
      Specified by:
      seed in interface HasSeed
      Returns:
      (undocumented)

    • featuresCol

      public final Param<String> featuresCol()
      Description copied from interface: HasFeaturesCol
      Param for features column name.
      Specified by:
      featuresCol in interface HasFeaturesCol
      Returns:
      (undocumented)

    • maxIter

      public final IntParam maxIter()
      Description copied from interface: HasMaxIter
      Param for maximum number of iterations (&gt;= 0).
      Specified by:
      maxIter in interface HasMaxIter
      Returns:
      (undocumented)

    • uid

      public String uid()
      Description copied from interface: Identifiable
      An immutable unique ID for the object and its derivatives.
      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • copy

      public BisectingKMeans copy(ParamMap extra)
      Description copied from interface: Params
      Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
      Specified by:
      copy in interface Params
      Specified by:
      copy in class Estimator<BisectingKMeansModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • setFeaturesCol

      public BisectingKMeans setFeaturesCol(String value)

    • setPredictionCol

      public BisectingKMeans setPredictionCol(String value)

    • setK

      public BisectingKMeans setK(int value)

    • setMaxIter

      public BisectingKMeans setMaxIter(int value)

    • setSeed

      public BisectingKMeans setSeed(long value)

    • setMinDivisibleClusterSize

      public BisectingKMeans setMinDivisibleClusterSize(double value)

    • setDistanceMeasure

      public BisectingKMeans setDistanceMeasure(String value)

    • setWeightCol

      public BisectingKMeans setWeightCol(String value)
      Sets the value of param weightCol(). If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

      Parameters:
      value - (undocumented)
      Returns:
      (undocumented)

    • fit

      public BisectingKMeansModel fit(Dataset<?> dataset)
      Description copied from class: Estimator
      Fits a model to the input data.
      Specified by:
      fit in class Estimator<BisectingKMeansModel>
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      public StructType transformSchema(StructType schema)
      Description copied from class: PipelineStage
      Check transform validity and derive the output schema from the input schema.

      We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

      Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

      Specified by:
      transformSchema in class PipelineStage
      Parameters:
      schema - (undocumented)
      Returns:
      (undocumented)