object KMeans extends Serializable
Top-level methods for calling K-means clustering.
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- KMeans.scala
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- val K_MEANS_PARALLEL: String
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- 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.
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- 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
RDDofVectortypes.- 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||")
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- @Since("2.1.0")
- 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
RDDofVectortypes.- 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.
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(Since version 9)