A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark. The algorithm starts from a single cluster that contains all points. Iteratively it finds divisible clusters on the bottom level and bisects each of them using k-means, until there are k leaf clusters in total or no leaf clusters are divisible. The bisecting steps of clusters on the same level are grouped together to increase parallelism. If bisecting all divisible clusters on the bottom level would result more than k leaf clusters, larger clusters get higher priority.
ml_bisecting_kmeans( x, formula = NULL, k = 4, max_iter = 20, seed = NULL, min_divisible_cluster_size = 1, features_col = "features", prediction_col = "prediction", uid = random_string("bisecting_bisecting_kmeans_"), ... )
The number of clusters to create
The maximum number of iterations to use.
A random seed. Set this value if you need your results to be reproducible across repeated calls.
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 (default: 1.0).
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
Prediction column name.
A character string used to uniquely identify the ML estimator.
Optional arguments, see Details.
The object returned depends on the class of
x is a
spark_connection, the function returns an instance of a
ml_estimator object. The object contains a pointer to
Estimator object and can be used to compose
x is a
ml_pipeline, the function returns a
the clustering estimator appended to the pipeline.
x is a
tbl_spark, an estimator is constructed then
immediately fit with the input
tbl_spark, returning a clustering model.
features specified: When
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
See http://spark.apache.org/docs/latest/ml-clustering.html for more information on the set of clustering algorithms.