org.apache.spark.mllib.clustering

StreamingKMeansModel

class StreamingKMeansModel extends KMeansModel with Logging

:: DeveloperApi :: StreamingKMeansModel extends MLlib's KMeansModel for streaming algorithms, so it can keep track of a continuously updated weight associated with each cluster, and also update the model by doing a single iteration of the standard k-means algorithm.

The update algorithm uses the "mini-batch" KMeans rule, generalized to incorporate forgetfullness (i.e. decay). The update rule (for each cluster) is:

c_t+1 = [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t] n_t+t = n_t * a + m_t

Where c_t is the previously estimated centroid for that cluster, n_t is the number of points assigned to it thus far, x_t is the centroid estimated on the current batch, and m_t is the number of points assigned to that centroid in the current batch.

The decay factor 'a' scales the contribution of the clusters as estimated thus far, by applying a as a discount weighting on the current point when evaluating new incoming data. If a=1, all batches are weighted equally. If a=0, new centroids are determined entirely by recent data. Lower values correspond to more forgetting.

Decay can optionally be specified by a half life and associated time unit. The time unit can either be a batch of data or a single data point. Considering data arrived at time t, the half life h is defined such that at time t + h the discount applied to the data from t is 0.5. The definition remains the same whether the time unit is given as batches or points.

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@DeveloperApi()
Linear Supertypes
Logging, KMeansModel, Serializable, Serializable, AnyRef, Any
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  1. StreamingKMeansModel
  2. Logging
  3. KMeansModel
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Instance Constructors

  1. new StreamingKMeansModel(clusterCenters: Array[Vector], clusterWeights: Array[Double])

Value Members

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

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. val clusterCenters: Array[Vector]

    Definition Classes
    StreamingKMeansModelKMeansModel
  9. val clusterWeights: Array[Double]

  10. def computeCost(data: RDD[Vector]): Double

    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

    Definition Classes
    KMeansModel
  11. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  13. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  17. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  18. def k: Int

    Total number of clusters.

    Total number of clusters.

    Definition Classes
    KMeansModel
  19. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  20. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  21. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  22. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  23. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  24. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  25. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  26. def logName: String

    Attributes
    protected
    Definition Classes
    Logging
  27. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  28. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  29. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  30. def logWarning(msg: ⇒ String): Unit

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    protected
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    Logging
  31. final def ne(arg0: AnyRef): Boolean

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  32. final def notify(): Unit

    Definition Classes
    AnyRef
  33. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  34. def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

    Definition Classes
    KMeansModel
  35. def predict(points: RDD[Vector]): RDD[Int]

    Maps given points to their cluster indices.

    Maps given points to their cluster indices.

    Definition Classes
    KMeansModel
  36. def predict(point: Vector): Int

    Returns the cluster index that a given point belongs to.

    Returns the cluster index that a given point belongs to.

    Definition Classes
    KMeansModel
  37. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  38. def toString(): String

    Definition Classes
    AnyRef → Any
  39. def update(data: RDD[Vector], decayFactor: Double, timeUnit: String): StreamingKMeansModel

    Perform a k-means update on a batch of data.

  40. final def wait(): Unit

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    @throws( ... )
  41. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  42. final def wait(arg0: Long): Unit

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Inherited from Logging

Inherited from KMeansModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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