R: Spark ML - K-Means Clustering

ml_kmeans {sparklyr} | R Documentation

## Spark ML – K-Means Clustering

### Description

Perform k-means clustering on a Spark DataFrame.

### Usage

    ml_kmeans(x, centers, iter.max = 100, features = dplyr::tbl_vars(x),
      compute.cost = TRUE, tolerance = 1e-04, ml.options = ml_options(), ...)

### Arguments

`x` | An object coercable to a Spark DataFrame (typically, a `tbl_spark`).
`centers` | The number of cluster centers to compute.
`iter.max` | The maximum number of iterations to use.
`features` | The name of features (terms) to use for the model fit.
`compute.cost` | Whether to compute cost for `k-means` model using Spark's computeCost.
`tolerance` | Param for the convergence tolerance for iterative algorithms.
`ml.options` | Optional arguments, used to affect the model generated. See `ml_options` for more details.
`...` | Optional arguments. The `data` argument can be used to specify the data to be used when `x` is a formula; this allows calls of the form `ml_linear_regression(y ~ x, data = tbl)`, and is especially useful in conjunction with `do`.

### Value

ml_model object of class `kmeans` with overloaded `print`, `fitted` and `predict` functions.

### References

Bahmani et al., Scalable K-Means++, VLDB 2012

### See Also

For information on how Spark k-means clustering is implemented, please see http://spark.apache.org/docs/latest/mllib-clustering.html#k-means.

Other Spark ML routines: `ml_als_factorization`, `ml_decision_tree`, `ml_generalized_linear_regression`, `ml_gradient_boosted_trees`, `ml_lda`, `ml_linear_regression`, `ml_logistic_regression`, `ml_multilayer_perceptron`, `ml_naive_bayes`, `ml_one_vs_rest`, `ml_pca`, `ml_random_forest`, `ml_survival_regression`

[Package sparklyr version 0.5.4 Index]