Packages

class KMeans extends Serializable with Logging

K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).

This is an iterative algorithm that will make multiple passes over the data, so any RDDs given to it should be cached by the user.

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@Since("0.8.0")
Source
KMeans.scala
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Instance Constructors

  1. new KMeans()

    Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, initializationMode: "k-means||", initializationSteps: 2, epsilon: 1e-4, seed: random, distanceMeasure: "euclidean"}.

    Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, initializationMode: "k-means||", initializationSteps: 2, epsilon: 1e-4, seed: random, distanceMeasure: "euclidean"}.

    Annotations
    @Since("0.8.0")

Type Members

  1. implicit class LogStringContext extends AnyRef
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    Logging

Value Members

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  4. def MDC(key: LogKey, value: Any): MDC
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  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
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  9. final def getClass(): Class[_ <: AnyRef]
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    @IntrinsicCandidate() @native()
  10. def getDistanceMeasure: String

    The distance suite used by the algorithm.

    The distance suite used by the algorithm.

    Annotations
    @Since("2.4.0")
  11. def getEpsilon: Double

    The distance threshold within which we've consider centers to have converged.

    The distance threshold within which we've consider centers to have converged.

    Annotations
    @Since("1.4.0")
  12. def getInitializationMode: String

    The initialization algorithm.

    The initialization algorithm. This can be either "random" or "k-means||".

    Annotations
    @Since("1.4.0")
  13. def getInitializationSteps: Int

    Number of steps for the k-means|| initialization mode

    Number of steps for the k-means|| initialization mode

    Annotations
    @Since("1.4.0")
  14. def getK: Int

    Number of clusters to create (k).

    Number of clusters to create (k).

    Annotations
    @Since("1.4.0")
    Note

    It is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster.

  15. def getMaxIterations: Int

    Maximum number of iterations allowed.

    Maximum number of iterations allowed.

    Annotations
    @Since("1.4.0")
  16. def getSeed: Long

    The random seed for cluster initialization.

    The random seed for cluster initialization.

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    @Since("1.4.0")
  17. def hashCode(): Int
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    @IntrinsicCandidate() @native()
  18. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  19. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  21. def isTraceEnabled(): Boolean
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  23. def logBasedOnLevel(level: Level)(f: => MessageWithContext): Unit
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  25. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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  26. def logDebug(entry: LogEntry): Unit
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  27. def logDebug(msg: => String): Unit
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  28. def logError(msg: => String, throwable: Throwable): Unit
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  29. def logError(entry: LogEntry, throwable: Throwable): Unit
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  30. def logError(entry: LogEntry): Unit
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  31. def logError(msg: => String): Unit
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  32. def logInfo(msg: => String, throwable: Throwable): Unit
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  33. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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  34. def logInfo(entry: LogEntry): Unit
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  35. def logInfo(msg: => String): Unit
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  36. def logName: String
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  37. def logTrace(msg: => String, throwable: Throwable): Unit
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  38. def logTrace(entry: LogEntry, throwable: Throwable): Unit
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  39. def logTrace(entry: LogEntry): Unit
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  40. def logTrace(msg: => String): Unit
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  41. def logWarning(msg: => String, throwable: Throwable): Unit
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  42. def logWarning(entry: LogEntry, throwable: Throwable): Unit
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  43. def logWarning(entry: LogEntry): Unit
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  44. def logWarning(msg: => String): Unit
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  45. final def ne(arg0: AnyRef): Boolean
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  46. final def notify(): Unit
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    @IntrinsicCandidate() @native()
  47. final def notifyAll(): Unit
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    @IntrinsicCandidate() @native()
  48. def run(data: RDD[Vector]): KMeansModel

    Train a K-means model on the given set of points; data should be cached for high performance, because this is an iterative algorithm.

    Train a K-means model on the given set of points; data should be cached for high performance, because this is an iterative algorithm.

    Annotations
    @Since("0.8.0")
  49. def setDistanceMeasure(distanceMeasure: String): KMeans.this.type

    Set the distance suite used by the algorithm.

    Set the distance suite used by the algorithm.

    Annotations
    @Since("2.4.0")
  50. def setEpsilon(epsilon: Double): KMeans.this.type

    Set the distance threshold within which we've consider centers to have converged.

    Set the distance threshold within which we've consider centers to have converged. If all centers move less than this Euclidean distance, we stop iterating one run.

    Annotations
    @Since("0.8.0")
  51. def setInitialModel(model: KMeansModel): KMeans.this.type

    Set the initial starting point, bypassing the random initialization or k-means|| The condition model.k == this.k must be met, failure results in an IllegalArgumentException.

    Set the initial starting point, bypassing the random initialization or k-means|| The condition model.k == this.k must be met, failure results in an IllegalArgumentException.

    Annotations
    @Since("1.4.0")
  52. def setInitializationMode(initializationMode: String): KMeans.this.type

    Set the initialization algorithm.

    Set the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.

    Annotations
    @Since("0.8.0")
  53. def setInitializationSteps(initializationSteps: Int): KMeans.this.type

    Set the number of steps for the k-means|| initialization mode.

    Set the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Default: 2.

    Annotations
    @Since("0.8.0")
  54. def setK(k: Int): KMeans.this.type

    Set the number of clusters to create (k).

    Set the number of clusters to create (k).

    Annotations
    @Since("0.8.0")
    Note

    It is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.

  55. def setMaxIterations(maxIterations: Int): KMeans.this.type

    Set maximum number of iterations allowed.

    Set maximum number of iterations allowed. Default: 20.

    Annotations
    @Since("0.8.0")
  56. def setSeed(seed: Long): KMeans.this.type

    Set the random seed for cluster initialization.

    Set the random seed for cluster initialization.

    Annotations
    @Since("1.4.0")
  57. final def synchronized[T0](arg0: => T0): T0
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  58. def toString(): String
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  59. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  60. final def wait(arg0: Long): Unit
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  61. final def wait(): Unit
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  62. def withLogContext(context: Map[String, String])(body: => Unit): Unit
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    Logging

Deprecated Value Members

  1. def finalize(): Unit
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

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