| 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.
|