emst_mlpack: Euclidean Minimum Spanning Tree

In genieclust: The Genie++ Hierarchical Clustering Algorithm with Noise Points Detection

Description

Provides access to an implementation of the Dual-Tree Borůvka algorithm based on kd-trees from MLPACK. It 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, verbose = FALSE)

Arguments

X	a numeric matrix (or an object coercible to one, e.g., a data frame with numeric-like columns)
verbose	logical; whether to print diagnostic messages

Value

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

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), 726, 2018.

genieclust documentation built on Jan. 13, 2021, 8:12 p.m.