Interface  Description 

LDAOptimizer 
:: DeveloperApi ::

Class  Description 

BisectingKMeans 
A bisecting kmeans 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$  
BisectingKMeansModel.SaveLoadV3_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 
Kmeans clustering with a kmeans++ like initialization mode
(the kmeans algorithm by Bahmani et al).

KMeansModel 
A clustering model for Kmeans.

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 Kmeans 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 kmeans 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 kmeans algorithm.
