emst_mlpack: Euclidean Minimum Spanning Tree

In genieclust: Fast and Robust Hierarchical Clustering with Noise Points Detection

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.

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