| spark.bisectingKmeans {SparkR} | R Documentation |
Fits a bisecting k-means clustering model against a SparkDataFrame.
Users can call summary to print a summary of the fitted model, predict to make
predictions on new data, and write.ml/read.ml to save/load fitted models.
Get fitted result from a bisecting k-means model. Note: A saved-loaded model does not support this method.
spark.bisectingKmeans(data, formula, ...)
## S4 method for signature 'SparkDataFrame,formula'
spark.bisectingKmeans(
data,
formula,
k = 4,
maxIter = 20,
seed = NULL,
minDivisibleClusterSize = 1
)
## S4 method for signature 'BisectingKMeansModel'
summary(object)
## S4 method for signature 'BisectingKMeansModel'
predict(object, newData)
## S4 method for signature 'BisectingKMeansModel'
fitted(object, method = c("centers", "classes"))
## S4 method for signature 'BisectingKMeansModel,character'
write.ml(object, path, overwrite = FALSE)
data |
a SparkDataFrame for training. |
formula |
a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', '-', '*', and '^'. Note that the response variable of formula is empty in spark.bisectingKmeans. |
... |
additional argument(s) passed to the method. |
k |
the desired number of leaf clusters. Must be > 1. The actual number could be smaller if there are no divisible leaf clusters. |
maxIter |
maximum iteration number. |
seed |
the random seed. |
minDivisibleClusterSize |
The minimum number of points (if greater than or equal to 1.0) or the minimum proportion of points (if less than 1.0) of a divisible cluster. Note that it is an expert parameter. The default value should be good enough for most cases. |
object |
a fitted bisecting k-means model. |
newData |
a SparkDataFrame for testing. |
method |
type of fitted results, |
path |
the directory where the model is saved. |
overwrite |
overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
spark.bisectingKmeans returns a fitted bisecting k-means model.
summary returns summary information of the fitted model, which is a list.
The list includes the model's k (number of cluster centers),
coefficients (model cluster centers),
size (number of data points in each cluster), cluster
(cluster centers of the transformed data; cluster is NULL if is.loaded is TRUE),
and is.loaded (whether the model is loaded from a saved file).
predict returns the predicted values based on a bisecting k-means model.
fitted returns a SparkDataFrame containing fitted values.
spark.bisectingKmeans since 2.2.0
summary(BisectingKMeansModel) since 2.2.0
predict(BisectingKMeansModel) since 2.2.0
fitted since 2.2.0
write.ml(BisectingKMeansModel, character) since 2.2.0
## Not run:
##D sparkR.session()
##D t <- as.data.frame(Titanic)
##D df <- createDataFrame(t)
##D model <- spark.bisectingKmeans(df, Class ~ Survived, k = 4)
##D summary(model)
##D
##D # get fitted result from a bisecting k-means model
##D fitted.model <- fitted(model, "centers")
##D showDF(fitted.model)
##D
##D # fitted values on training data
##D fitted <- predict(model, df)
##D head(select(fitted, "Class", "prediction"))
##D
##D # save fitted model to input path
##D path <- "path/to/model"
##D write.ml(model, path)
##D
##D # can also read back the saved model and print
##D savedModel <- read.ml(path)
##D summary(savedModel)
## End(Not run)