A function to compute the NID between two classifications

Implements an efficient O(n) algorithm based on bucket-sorting for
fast computation of standard clustering comparison measures. Available measures
include adjusted Rand index (ARI), normalized information distance (NID),
normalized mutual information (NMI), adjusted mutual information (AMI),
normalized variation information (NVI) and entropy, as described in Vinh et al (2009)
<doi:10.1145/1553374.1553511>. Include AMI (Adjusted Mutual Information) since version 0.1.2,
a modified version of ARI (MARI), as described in Sundqvist et al. <doi:10.1007/s00180-022-01230-7>
and simple Chi-square distance since version 1.0.0.

Julien Chiquet

aricode

Efficient Computations of Standard Clustering Comparison
Measures

Guillem Rigaill

Martina Sundqvist

Valentin Dervieux

Florent Bersani

NID function

<dl><dt>c1</dt>
<dd>a vector containing the labels of the first classification. Must be a vector of characters, integers, numerics, or a factor, but not a list.</dd>
<dt>c2</dt>
<dd>a vector containing the labels of the second classification.</dd></dl>

Arguments

Normalized information distance (NID) — NID

<dl>

<dt>c1</dt>
<dd>a vector containing the labels of the first classification. Must be a vector of characters, integers, numerics, or a factor, but not a list.</dd>


<dt>c2</dt>
<dd>a vector containing the labels of the second classification.</dd>

</dl>

NID: Normalized information distance (NID)

Description

Usage

Value

Arguments

See Also

Examples