emst_mlpack

Provides access to the implementation of the Dual-Tree Boruvka
algorithm from the <code>mlpack</code> 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 <code>mst()</code>.

A retake on the Genie algorithm
(Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>) - a robust
hierarchical clustering method
(Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>).
Now faster and more memory efficient; determining the whole hierarchy
for datasets of 10M points in low dimensional Euclidean spaces or
100K points in high-dimensional ones takes only 1-2 minutes.
Allows clustering with respect to mutual reachability distances
so that it can act as a noise point detector or a robustified version of
'HDBSCAN*' (that is able to detect a predefined number of
clusters and hence it does not dependent on the somewhat
fragile 'eps' parameter).
The package also features an implementation of inequality indices
(the Gini, Bonferroni index), external cluster validity measures
(e.g., the normalised clustering accuracy and partition similarity scores
such as the adjusted Rand, Fowlkes-Mallows, adjusted mutual information,
and the pair sets index),
and internal cluster validity indices (e.g., the Calinski-Harabasz,
Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices).
See also the 'Python' version of 'genieclust' available on 'PyPI', which
supports sparse data, more metrics, and even larger datasets.

Marek Gagolewski

genieclust

Fast and Robust Hierarchical Clustering with Noise Points
Detection

Maciej Bartoszuk

Anna Cena

Peter M. Larsen

emst_mlpack function

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

Arguments

<a href="https://www.gagolewski.com/">Marek Gagolewski</a> and other contributors

Author

Euclidean Minimum Spanning Tree — emst_mlpack

<dl>

<dt>X</dt>
<dd>a numeric matrix (or an object coercible to one,
e.g., a data frame with numeric-like columns)</dd>


<dt>leaf_size</dt>
<dd>size of leaves in the kd-tree,
controls the trade-off between speed and memory consumption</dd>


<dt>naive</dt>
<dd>logical; whether to use the naive, quadratic-time algorithm</dd>


<dt>verbose</dt>
<dd>logical; whether to print diagnostic messages</dd>

</dl>

<a href='https://www.gagolewski.com/'>Marek Gagolewski</a> and other contributors

emst_mlpack: Euclidean Minimum Spanning Tree

Description

Usage

Value

Arguments

Author

References

See Also