| Interface | Description |
|---|---|
| LDAOptimizer |
An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
hold optimizer-specific parameters for users to set.
|
| 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$ | |
| BisectingKMeansModel.SaveLoadV3_0$ | |
| DistributedLDAModel |
Distributed LDA model.
|
| EMLDAOptimizer |
Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters.
|
| 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.Cluster$ | |
| 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 |
An online optimizer for LDA.
|
| 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.
|