emst_mlpack: Euclidean Minimum Spanning Tree#

Description#

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().

Usage#

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

Arguments#


`X`	a numeric matrix (or an object coercible to one, e.g., a data frame with numeric-like columns)
`leaf_size`	size of leaves in the kd-tree, controls the trade-off between speed and memory consumption
`naive`	logical; whether to use the naive, quadratic-time algorithm
`verbose`	logical; whether to print diagnostic messages

Value#

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

Author(s)#

Marek Gagolewski and other contributors

References#

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.