emst_mlpack: Euclidean Minimum Spanning Tree

View source: R/mst.R

emst_mlpackR Documentation

Euclidean Minimum Spanning Tree


Provides access to the implementation of the Dual-Tree Boruvka algorithm from the mlpack package (if available). It is based on kd-trees and is fast for (very) low-dimensional Euclidean spaces. For higher dimensional spaces (say, over 5 features) or other metrics, use the parallelised Prim-like algorithm implemented in mst().


emst_mlpack(X, leaf_size = 1, naive = FALSE, verbose = FALSE)



a numeric matrix (or an object coercible to one, e.g., a data frame with numeric-like columns)


size of leaves in the kd-tree, controls the trade-off between speed and memory consumption


logical; whether to use the naive, quadratic-time algorithm


logical; whether to print diagnostic messages


An object of class mst, see mst() for details.


Marek Gagolewski and other contributors


March W.B., Ram P., Gray A.G., Fast Euclidean Minimum Spanning Tree: Algorithm, Analysis, and Applications, Proc. ACM SIGKDD'10, 2010, 603-611, https://mlpack.org/papers/emst.pdf.

Curtin R.R., Edel M., Lozhnikov M., Mentekidis Y., Ghaisas S., Zhang S., mlpack 3: A fast, flexible machine learning library, Journal of Open Source Software 3(26), 2018, 726.

See Also

The official online manual of genieclust at https://genieclust.gagolewski.com/

Gagolewski M., genieclust: Fast and robust hierarchical clustering, SoftwareX 15:100722, 2021, doi: 10.1016/j.softx.2021.100722.

genieclust documentation built on Sept. 5, 2022, 9:05 a.m.