package clustering

  1. Public
  2. All

Type Members

  1. 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.

  2. class BisectingKMeansModel extends Serializable with Logging

    Clustering model produced by BisectingKMeans.

  3. class DistributedLDAModel extends LDAModel

    Distributed LDA model.

  4. final class EMLDAOptimizer extends LDAOptimizer

    :: DeveloperApi ::

  5. class GaussianMixture extends Serializable

    This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs).

  6. class GaussianMixtureModel extends Serializable with Saveable

    Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points are drawn from each Gaussian i=1.

  7. class KMeans extends Serializable with Logging

    K-means clustering with support for multiple parallel runs and a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).

  8. class KMeansModel extends Saveable with Serializable with PMMLExportable

    A clustering model for K-means.

  9. class LDA extends Logging

    Latent Dirichlet Allocation (LDA), a topic model designed for text documents.

  10. abstract class LDAModel extends Saveable

    Latent Dirichlet Allocation (LDA) model.

  11. sealed trait LDAOptimizer extends AnyRef

    :: DeveloperApi ::

  12. class LocalLDAModel extends LDAModel with Serializable

    Local LDA model.

  13. final class OnlineLDAOptimizer extends LDAOptimizer

    :: DeveloperApi ::

  14. class PowerIterationClustering extends Serializable

    Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by Lin and Cohen.

  15. class PowerIterationClusteringModel extends Saveable with Serializable

    Model produced by PowerIterationClustering.

  16. class StreamingKMeans extends Logging with Serializable

    StreamingKMeans provides methods for configuring a streaming k-means analysis, training the model on streaming, and using the model to make predictions on streaming data.

  17. class StreamingKMeansModel extends KMeansModel with Logging

    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.

Value Members

  1. object DistributedLDAModel extends Loader[DistributedLDAModel]

    @Experimental() @Since( "1.5.0" )
  2. object GaussianMixtureModel extends Loader[GaussianMixtureModel] with Serializable

    @Since( "1.4.0" )
  3. object KMeans extends Serializable

    Top-level methods for calling K-means clustering.

  4. object KMeansModel extends Loader[KMeansModel] with Serializable

    @Since( "1.4.0" )
  5. object LocalLDAModel extends Loader[LocalLDAModel] with Serializable

    @Experimental() @Since( "1.5.0" )
  6. object PowerIterationClustering extends Logging with Serializable

    @Since( "1.3.0" )
  7. object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] with Serializable

    @Since( "1.4.0" )