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.