Spark ML -- K-Means Clustering

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; currently unused.

Value

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

Description

Perform k-means clustering on a Spark DataFrame.

References

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