Interface | Description |
---|---|
BisectingKMeansParams |
Common params for BisectingKMeans and BisectingKMeansModel
|
GaussianMixtureParams |
Common params for GaussianMixture and GaussianMixtureModel
|
KMeansParams |
Common params for KMeans and KMeansModel
|
LDAParams | |
PowerIterationClusteringParams |
Common params for PowerIterationClustering
|
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 |
Model fitted by BisectingKMeans.
|
BisectingKMeansSummary |
Summary of BisectingKMeans.
|
ClusterData |
Helper class for storing model data
|
ClusteringSummary |
Summary of clustering algorithms.
|
DistributedLDAModel |
Distributed model fitted by
LDA . |
ExpectationAggregator |
ExpectationAggregator computes the partial expectation results.
|
GaussianMixture |
Gaussian Mixture clustering.
|
GaussianMixtureModel |
Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points
are drawn from each Gaussian i with probability weights(i).
|
GaussianMixtureSummary |
Summary of GaussianMixture.
|
InternalKMeansModelWriter |
A writer for KMeans that handles the "internal" (or default) format
|
KMeans |
K-means clustering with support for k-means|| initialization proposed by Bahmani et al.
|
KMeansAggregator |
KMeansAggregator computes the distances and updates the centers for blocks
in sparse or dense matrix in an online fashion.
|
KMeansModel |
Model fitted by KMeans.
|
KMeansSummary |
Summary of KMeans.
|
LDA |
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
|
LDAModel |
Model fitted by
LDA . |
LocalLDAModel |
Local (non-distributed) model fitted by
LDA . |
PMMLKMeansModelWriter |
A writer for KMeans that handles the "pmml" format
|
PowerIterationClustering |
Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by
Lin and Cohen.
|