In UBC-MDS/kmeans_R: R package aimed towards a user-friendly way of exploring and implementing k-means clustering.

Installation

Install this package directly from GitHub:

devtools::install_github("UBC-MDS/kmeans_R")

Usage

Simple example demonstrating the functionality of this package:

library(kmeansR)

# generate synthetic data with three clusters
synth_data <- data.frame(
  x = c(rnorm(20,1,1), rnorm(30,6,3), rnorm(15,10,2)),
  y = c(rnorm(20,5,2), rnorm(30,2,2), rnorm(15,8,3))
)

# initialize the cluster centers
centers <- kmeans_init(data= synth_data, K = 3)
# cluster the data points
clusters <- kmeans_cluster(data = synth_data, centers = centers)
# generate summary results
report <- kmeans_report(synth_data, clusters$assignments)

# plot the clustered data
report$plot

Overview

kmeans_R is an R package aimed towards a user-friendly way of exploring and implementing k-means clustering.

The package integrates and simplifies different functions, such as kmeans and KMeans_rcpp, into one easy-to-use package.

The package includes the following functions:

• kmeans_init(data, K, method = "kmeanspp", seed = NULL) Selects initial values (or seeds) for k-means clustering based on the input data object. K number of initial values are chosen by applying the specified method. Returns a matrix with coordinates for initialization values, where each row is an initialization value and the columns correspond with the columns of the input data object.

• kmeans_cluster(data, centers, max_iter=100) Classifies each observation in data by performing k-means clustering. The number of clusters is derived from the number of initial centers specified in centers. max_iter defaults to 100 and simply places on an upper bound on the number of iterations that take place. Returns a data frame containing the original data and a column of assigned cluster labels.

• kmeans_report(data, assignments) Visualizes clustered data using original data and the assignments as determined by the cluster function. Returns a list containing a data frame with original data and assignments, a plot object if data is two dimensional, and a summary table containing counts of the number of points in each cluster.

UBC-MDS/kmeans_R documentation built on May 22, 2019, 2:26 p.m.