R/hierarchical_clustering.R In scclust: Size-Constrained Clustering

Documented in hierarchical_clustering

# ==============================================================================
# scclust for R -- R wrapper for the scclust library
# https://github.com/fsavje/scclust-R
#
# Copyright (C) 2016-2017  Fredrik Savje -- http://fredriksavje.com
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see http://www.gnu.org/licenses/
# ==============================================================================

#' Hierarchical size-constrained clustering
#'
#' \code{hierarchical_clustering} serves two purposes. Its primary use is to
#' refine existing clusters subject to clustering constraints. That is, given
#' a (non-optimal) clustering satisfying some constraints, the function splits
#' clusters so to decrease within-cluster distances without violating the
#' constraints. The function can also be used to derive size-constrained
#' clusterings from scratch. In both cases, it uses a hierarchical clustering
#' algorithm.
#'
#' While \code{hierarchical_clustering} can be used to derive size-constrained
#' clusters from scratch, its main purpose is to be used together with
#' \code{\link{sc_clustering}}. The clusters produced by the main clustering
#' function are guaranteed to be close to optimal (in particular, within a
#' constant factor of the optimal solution). However, it is occasionally
#' possible to refine the clustering. In particular,
#' \code{\link{sc_clustering}} tends produce large clusters in regions of the
#' metric space with many data points. In some cases, it is beneficial to divide
#' these clusters into smaller groups. \code{hierarchical_clustering} splits
#' these larger clusters so that all within-cluster distances weakly decrease
#' while respecting the overall the size constraint.
#'
#' \code{hierarchical_clustering} implements a divisive hierarchical clustering
#' algorithm that respect size constraints. Starting from any clustering
#' satisfying the size constraints (which may be a single cluster containing
#' all data points), the function searches for clusters that can be broken into
#' two or more new clusters without violating the constraints. When such a
#' cluster is found, it breaks the cluster into two new clusters. It continues
#' in this fashion until all remaining clusters are unbreakable.
#'
#' Breakable clusters are broken in three stages. First, it tries to find two
#' data points as far as possible from each other. The two points are called
#' \emph{centers}, and they are the starting points for the new clusters. The
#' remaining data points in the old cluster will be assigned to one of the
#' centers. In the second stage, each center picks the closest data points so
#' that the new two clusters exactly satisfy the size constraint. In the last
#' stage, data points that still are in the old cluster are assigned to the
#' cluster containing the closest center. The final stage is done either for
#' each point one-by-one or in batches (see below). When all data points are
#' assigned to a cluster, the old cluster is removed and the two new are added
#' to the clustering. If one or both of the new clusters are breakable, they
#' will go through the same procedure again.
#'
#' In some applications, it is desirable to avoid clusters that contain a number
#' of data points that are not multiples of \code{size_constraint}. After the
#' second stage described in the previous paragraph, both partial clusters are
#' exact multiples of the size constraint. By assigning remaining data points
#' in the third stage in batches of \code{size_constraint}, the function ensures,
#' to the greatest extent possible, that the size of the final clusters are
#' multiples of the size constraint.
#'
#' @param distances
#'    a \code{\link[distances]{distances}} object with distances between the
#'    data points.
#' @param size_constraint
#'    an integer with the required minimum cluster size.
#' @param batch_assign
#'    a logical indicating whether data points should be assigned in batches
#'    when spliting clusters (see below for details).
#' @param existing_clustering
#'    a \code{\link{scclust}} object containing a non-empty clustering to
#'    refine. If \code{NULL}, the function derives a clustering from scratch.
#'
#' @return
#'    Returns a \code{\link{scclust}} object with the derived clustering.
#'
#' @seealso
#'    \code{\link{sc_clustering}} is the main clustering function in the
#'    package.
#'
#' @examples
#' # Make example data
#' my_data <- data.frame(x1 = rnorm(10000),
#'                       x2 = rnorm(10000),
#'                       x3 = rnorm(10000))
#'
#' # Construct distance metric
#' my_dist <- distances(my_data)
#'
#' # Make clustering with sc_clustering
#' my_clustering <- sc_clustering(my_dist, 3)
#'
#' # Refine clustering with hierarchical_clustering
#' my_refined_clustering <- hierarchical_clustering(my_dist,
#'                                                  size_constraint = 3,
#'                                                  existing_clustering = my_clustering)
#'
#' # Make clustering from scratch with hierarchical_clustering
#' my_other_clustering <- hierarchical_clustering(my_dist, 3)
#'
#' @keywords cluster
#' @export
hierarchical_clustering <- function(distances,
size_constraint,
batch_assign = TRUE,
existing_clustering = NULL) {
ensure_distances(distances)
num_data_points <- length(distances)
size_constraint <- coerce_size_constraint(size_constraint, num_data_points)
batch_assign <- coerce_scalar_indicator(batch_assign)
if (!is.null(existing_clustering)) {
ensure_scclust(existing_clustering, num_data_points)
}

clustering <- .Call(Rscc_hierarchical_clustering,
distances,
size_constraint,
batch_assign,
existing_clustering)

make_scclust(clustering$cluster_labels, clustering$cluster_count,
attr(distances, "ids", exact = TRUE))
}


Try the scclust package in your browser

Any scripts or data that you put into this service are public.

scclust documentation built on May 2, 2019, 4:04 p.m.