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| author | Felix Cheung <felixcheung_m@hotmail.com> | 2017-04-04 22:32:46 -0700 |
|---|---|---|
| committer | Felix Cheung <felixcheung@apache.org> | 2017-04-04 22:32:46 -0700 |
| commit | c1b8b667506ed95c6c2808e7d3db8463435e73f6 (patch) | |
| tree | 459d4f24ac83a4add957250029c094df46017d37 /R | |
| parent | b28bbffbadf7ebc4349666e8f17111f6fca18c9a (diff) | |
| download | spark-c1b8b667506ed95c6c2808e7d3db8463435e73f6.tar.gz spark-c1b8b667506ed95c6c2808e7d3db8463435e73f6.tar.bz2 spark-c1b8b667506ed95c6c2808e7d3db8463435e73f6.zip | |
[SPARKR][DOC] update doc for fpgrowth
## What changes were proposed in this pull request?
minor update
zero323
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes #17526 from felixcheung/rfpgrowthfollowup.
Diffstat (limited to 'R')
| -rw-r--r-- | R/pkg/R/mllib_clustering.R | 6 | ||||
| -rw-r--r-- | R/pkg/R/mllib_fpm.R | 4 |
2 files changed, 5 insertions, 5 deletions
diff --git a/R/pkg/R/mllib_clustering.R b/R/pkg/R/mllib_clustering.R index 0ebdb5a273..97c9fa1b45 100644 --- a/R/pkg/R/mllib_clustering.R +++ b/R/pkg/R/mllib_clustering.R @@ -498,11 +498,7 @@ setMethod("write.ml", signature(object = "KMeansModel", path = "character"), #' @export #' @examples #' \dontrun{ -#' # nolint start -#' # An example "path/to/file" can be -#' # paste0(Sys.getenv("SPARK_HOME"), "/data/mllib/sample_lda_libsvm_data.txt") -#' # nolint end -#' text <- read.df("path/to/file", source = "libsvm") +#' text <- read.df("data/mllib/sample_lda_libsvm_data.txt", source = "libsvm") #' model <- spark.lda(data = text, optimizer = "em") #' #' # get a summary of the model diff --git a/R/pkg/R/mllib_fpm.R b/R/pkg/R/mllib_fpm.R index 96251b2c7c..dfcb45a1b6 100644 --- a/R/pkg/R/mllib_fpm.R +++ b/R/pkg/R/mllib_fpm.R @@ -27,6 +27,10 @@ setClass("FPGrowthModel", slots = list(jobj = "jobj")) #' FP-growth #' #' A parallel FP-growth algorithm to mine frequent itemsets. +#' \code{spark.fpGrowth} fits a FP-growth model on a SparkDataFrame. Users can +#' \code{spark.freqItemsets} to get frequent itemsets, \code{spark.associationRules} to get +#' association rules, \code{predict} to make predictions on new data based on generated association +#' rules, and \code{write.ml}/\code{read.ml} to save/load fitted models. #' For more details, see #' \href{https://spark.apache.org/docs/latest/mllib-frequent-pattern-mining.html#fp-growth}{ #' FP-growth}. |