Spark ML -- Bisecting K-Means Clustering
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 = 4L, max_iter = 20L, seed = NULL, min_divisible_cluster_size = 1, features_col = "features", prediction_col = "prediction", uid = random_string("bisecting_bisecting_kmeans_"), ...)
ml_pipeline, or 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.
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; currently unused.
The object returned depends on the class of
spark_connection, the function returns an instance of a
ml_estimatorobject. The object contains a pointer to a Spark
Estimatorobject and can be used to compose
ml_pipeline, the function returns a
ml_pipelinewith the clustering estimator appended to the pipeline.
tbl_spark, an estimator is constructed then immediately fit with the input
tbl_spark, returning a clustering model.
formulais specified, the input
tbl_sparkis first transformed using a
RFormulatransformer before being fit by the estimator. The object returned in this case is a
ml_modelwhich 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.