Class

org.apache.spark.mllib.clustering

BisectingKMeans

Related Doc: package clustering

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class BisectingKMeans extends Logging

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.

Annotations
@Since( "1.6.0" )
Source
BisectingKMeans.scala
See also

Steinbach, Karypis, and Kumar, A comparison of document clustering techniques, KDD Workshop on Text Mining, 2000.

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Instance Constructors

  1. new BisectingKMeans()

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    Constructs with the default configuration

    Constructs with the default configuration

    Annotations
    @Since( "1.6.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  8. def finalize(): Unit

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  9. final def getClass(): Class[_]

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  10. def getK: Int

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    Gets the desired number of leaf clusters.

    Gets the desired number of leaf clusters.

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    @Since( "1.6.0" )

  11. def getMaxIterations: Int

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    Gets the max number of k-means iterations to split clusters.

    Gets the max number of k-means iterations to split clusters.

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    @Since( "1.6.0" )

  12. def getMinDivisibleClusterSize: Double

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    Gets the minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster.

    Gets the minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster.

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    @Since( "1.6.0" )

  13. def getSeed: Long

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    Gets the random seed.

    Gets the random seed.

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    @Since( "1.6.0" )

  14. def hashCode(): Int

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  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  17. def isTraceEnabled(): Boolean

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  18. def log: Logger

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  19. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  20. def logDebug(msg: ⇒ String): Unit

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  21. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  22. def logError(msg: ⇒ String): Unit

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  23. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  24. def logInfo(msg: ⇒ String): Unit

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  25. def logName: String

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  26. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  27. def logTrace(msg: ⇒ String): Unit

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  28. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  29. def logWarning(msg: ⇒ String): Unit

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  30. final def ne(arg0: AnyRef): Boolean

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  31. final def notify(): Unit

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  32. final def notifyAll(): Unit

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  33. def run(data: JavaRDD[Vector]): BisectingKMeansModel

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    Java-friendly version of run().

  34. def run(input: RDD[Vector]): BisectingKMeansModel

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    Runs the bisecting k-means algorithm.

    Runs the bisecting k-means algorithm.

    input

    RDD of vectors

    returns

    model for the bisecting kmeans

    Annotations
    @Since( "1.6.0" )

  35. def setK(k: Int): BisectingKMeans.this.type

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    Sets the desired number of leaf clusters (default: 4).

    Sets the desired number of leaf clusters (default: 4). The actual number could be smaller if there are no divisible leaf clusters.

    Annotations
    @Since( "1.6.0" )

  36. def setMaxIterations(maxIterations: Int): BisectingKMeans.this.type

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    Sets the max number of k-means iterations to split clusters (default: 20).

    Sets the max number of k-means iterations to split clusters (default: 20).

    Annotations
    @Since( "1.6.0" )

  37. def setMinDivisibleClusterSize(minDivisibleClusterSize: Double): BisectingKMeans.this.type

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    Sets the minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster (default: 1).

    Sets the minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster (default: 1).

    Annotations
    @Since( "1.6.0" )

  38. def setSeed(seed: Long): BisectingKMeans.this.type

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    Sets the random seed (default: hash value of the class name).

    Sets the random seed (default: hash value of the class name).

    Annotations
    @Since( "1.6.0" )

  39. final def synchronized[T0](arg0: ⇒ T0): T0

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  40. def toString(): String

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  41. final def wait(): Unit

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  42. final def wait(arg0: Long, arg1: Int): Unit

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  43. final def wait(arg0: Long): Unit

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