Class | Description |
---|---|
DistributedLDAModel |
:: Experimental ::
|
ExpectationSum | |
GaussianMixture |
:: Experimental ::
|
GaussianMixtureModel |
:: Experimental ::
|
KMeans |
K-means clustering with support for multiple parallel runs and a k-means++ like initialization
mode (the k-means|| algorithm by Bahmani et al).
|
KMeansModel |
A clustering model for K-means.
|
LDA |
:: Experimental ::
|
LDA.EMOptimizer |
Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters.
|
LDAModel |
:: Experimental ::
|
LocalKMeans |
An utility object to run K-means locally.
|
LocalLDAModel |
:: Experimental ::
|
PowerIterationClustering |
:: Experimental ::
|
PowerIterationClustering.Assignment |
:: Experimental ::
Cluster assignment.
|
PowerIterationClusteringModel |
:: Experimental ::
|
StreamingKMeans |
:: Experimental ::
|
StreamingKMeansModel |
:: Experimental ::
|
VectorWithNorm |
A vector with its norm for fast distance computation.
|