# ml_kmeans

##### Spark ML -- K-Means Clustering

K-means clustering with support for k-means|| initialization proposed by Bahmani et al. Using `ml_kmeans()` with the formula interface requires Spark 2.0+.

##### Usage

```
ml_kmeans(x, formula = NULL, k = 2, max_iter = 20, tol = 1e-04,
init_steps = 2, init_mode = "k-means||", seed = NULL,
features_col = "features", prediction_col = "prediction",
uid = random_string("kmeans_"), ...)
```ml_compute_cost(model, dataset)

##### Arguments

- x
A

`spark_connection`

,`ml_pipeline`

, or a`tbl_spark`

.- formula
Used when

`x`

is a`tbl_spark`

. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.- k
The number of clusters to create

- max_iter
The maximum number of iterations to use.

- tol
Param for the convergence tolerance for iterative algorithms.

- init_steps
Number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.

- init_mode
Initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.

- seed
A random seed. Set this value if you need your results to be reproducible across repeated calls.

- features_col
Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by

`ft_r_formula`

.- prediction_col
Prediction column name.

- uid
A character string used to uniquely identify the ML estimator.

- ...
Optional arguments, see Details.

- model
A fitted K-means model returned by

`ml_kmeans()`

- dataset
Dataset on which to calculate K-means cost

##### Value

The object returned depends on the class of `x`

.

`spark_connection`

: When`x`

is a`spark_connection`

, the function returns an instance of a`ml_estimator`

object. The object contains a pointer to a Spark`Estimator`

object and can be used to compose`Pipeline`

objects.`ml_pipeline`

: When`x`

is a`ml_pipeline`

, the function returns a`ml_pipeline`

with the clustering estimator appended to the pipeline.`tbl_spark`

: When`x`

is a`tbl_spark`

, an estimator is constructed then immediately fit with the input`tbl_spark`

, returning a clustering model.`tbl_spark`

, with`formula`

or`features`

specified: When`formula`

is specified, the input`tbl_spark`

is first transformed using a`RFormula`

transformer before being fit by the estimator. The object returned in this case is a`ml_model`

which is a wrapper of a`ml_pipeline_model`

. This signature does not apply to`ml_lda()`

.

`ml_compute_cost()`

returns the K-means cost (sum of
squared distances of points to their nearest center) for the model
on the given data.

##### See Also

See http://spark.apache.org/docs/latest/ml-clustering.html for more information on the set of clustering algorithms.

Other ml clustering algorithms: `ml_bisecting_kmeans`

,
`ml_gaussian_mixture`

, `ml_lda`

##### Examples

```
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
ml_kmeans(iris_tbl, Species ~ .)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package sparklyr, version 1.1.0, License: Apache License 2.0 | file LICENSE*