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object KMeans extends Serializable

Top-level methods for calling K-means clustering.

Annotations
@Since("0.8.0")
Source
KMeans.scala
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Value Members

  1. val K_MEANS_PARALLEL: String
    Annotations
    @Since("0.8.0")
  2. val RANDOM: String
    Annotations
    @Since("0.8.0")
  3. def train(data: RDD[Vector], k: Int, maxIterations: Int): KMeansModel

    Trains a k-means model using specified parameters and the default values for unspecified.

    Trains a k-means model using specified parameters and the default values for unspecified.

    Annotations
    @Since("0.8.0")
  4. def train(data: RDD[Vector], k: Int, maxIterations: Int, initializationMode: String): KMeansModel

    Trains a k-means model using the given set of parameters.

    Trains a k-means model using the given set of parameters.

    data

    Training points as an RDD of Vector types.

    k

    Number of clusters to create.

    maxIterations

    Maximum number of iterations allowed.

    initializationMode

    The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

    Annotations
    @Since("2.1.0")
  5. def train(data: RDD[Vector], k: Int, maxIterations: Int, initializationMode: String, seed: Long): KMeansModel

    Trains a k-means model using the given set of parameters.

    Trains a k-means model using the given set of parameters.

    data

    Training points as an RDD of Vector types.

    k

    Number of clusters to create.

    maxIterations

    Maximum number of iterations allowed.

    initializationMode

    The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

    seed

    Random seed for cluster initialization. Default is to generate seed based on system time.

    Annotations
    @Since("2.1.0")