Package org.apache.spark.mllib.clustering

Interface Summary

Interface	Description
LDAOptimizer	:: DeveloperApi ::

Class Summary

Class	Description
BisectingKMeans	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.
BisectingKMeansModel	Clustering model produced by BisectingKMeans.
BisectingKMeansModel.SaveLoadV1_0$
BisectingKMeansModel.SaveLoadV2_0$
DistributedLDAModel	Distributed LDA model.
EMLDAOptimizer	:: DeveloperApi ::
ExpectationSum
GaussianMixture	This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs).
GaussianMixtureModel	Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are the respective mean and covariance for each Gaussian distribution i=1..k.
KMeans	K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).
KMeansModel	A clustering model for K-means.
KMeansModel.SaveLoadV1_0$
KMeansModel.SaveLoadV2_0$
LDA	Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
LDAModel	Latent Dirichlet Allocation (LDA) model.
LDAUtils	Utility methods for LDA.
LocalKMeans	An utility object to run K-means locally.
LocalLDAModel	Local LDA model.
OnlineLDAOptimizer	:: DeveloperApi ::
PowerIterationClustering	Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by Lin and Cohen.
PowerIterationClustering.Assignment	Cluster assignment.
PowerIterationClustering.Assignment$
PowerIterationClusteringModel	Model produced by PowerIterationClustering.
PowerIterationClusteringModel.SaveLoadV1_0$
StreamingKMeans	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.
StreamingKMeansModel	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.