emstreeR (version 2.2.0)

emstreeR-package: Euclidean Minimum Spanning Tree

Description

The emstreeR package allows R users to fast and easily compute an Euclidean Minimum Spanning Tree from data.

Arguments

Introduction

This package relies on RcppMLPACK to provide an R interface to the Dual-Tree Boruvka algorithm (March, Ram, Gray, 2010) implemented in 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013). The Dual-Tree Boruvka is theoretically and emiprically the fastest algorithm for computing an Euclidean Minimum Spanning Tree (EMST).

Computing the Minimum Spanning Tree

ComputeMST is the main function of this package. It is a fast wrapper to its C++ homonym from 'mlpack' for computing an Euclidean Minimum Spanning Tree. Compared to functions in other MST related R packages, ComputeMST is easier to use because you can pass your data as a numeric matrix or a data.frame, which are the most common data input formats in the wild. You do not have to put it into a graph format as you otherwise would in other packages.

Plotting

'emstreeR' also provides wrapper functions and an S3 method for plotting the resulting MST from ComputeMST.

  • plot.MST is an S3 method to the generic function plot and produces 2D scatter plots with segments between the points in a 'base' R style, following the linkage order in the MST.

  • plotMST3D produces a 3D point cloud with segments between the points, following the linkage order in the MST and using the 'scatterplot3d' package style for plotting.

  • stat_MST is a 'ggplot2' Stat extension which produces 2D scatter plots with segments based on the linkage order in the MST using the 'ggplot2' style.

References

March, W. B., and Ram, P., and Gray, A. G. (2010). Fast euclidian minimum spanning tree: algorithm analysis, and applications. 16th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, July 25-28 2010. Washington, DC, USA. doi:10.1145/1835804.1835882.

Curtin, R. R. et al. (2013). Mlpack: A scalable C++ machine learning library. Journal of Machine Learning Research, v. 14, 2013.

See Also

Useful links: