A package for efficient computations of standard clustering comparison measures
Stable version on the CRAN.
install.packages("aricode")
The development version is available via:
devtools::install_github("jchiquet/aricode")
Computation of measures for clustering comparison (ARI, AMI, NID and
even the (\chi^2) distance) are usually based on the contingency
table. Traditional implementations (e.g., function adjustedRandIndex
of package mclust) are in (\Omega(n + u v)) where
In aricode we propose an implementation, based on radix sort, that
is in (\Theta(n)) in time and space.
Importantly, the complexity does not depends on (u) and (v). Our
implementation of the ARI for instance is one or two order of magnitude
faster than some standard implementation in R
.
The functions included in aricode are:
ARI
: computes the adjusted rand indexChi2
: computes the Chi-square statisticsMARI/MARIraw
: computes the modified adjusted rand index (Sundqvist
et al, in preparation)NVI
: computes the the normalized variation informationNID
: computes the normalized information distanceNMI
: computes the normalized mutual informationAMI
: computes the adjusted mutual informationexpected_MI
: computes the expected mutual informationentropy
: computes the conditional and joint entropiesclustComp
: computes all clustering comparison measures at onceHere are some timings to compare the cost of computing the adjusted Rand
Index with aricode or with the commonly used function
adjustedRandIndex
of the mclust package: the cost of the latter can
be prohibitive for large vectors:
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