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cleanzr/clevr

Clustering and Link Prediction Evaluation in R

clustering-evaluationentity-resolutionevaluation-metricslink-predictionr-packagerecord-linkage

clevr: Clustering and Link Prediction Evaluation in R

clevr implements functions for evaluating link prediction and clustering
algorithms in R. It includes efficient implementations of common
performance measures, such as:

  • pairwise precision, recall, F-measure;
  • homogeneity, completeness and V-measure;
  • (adjusted) Rand index;
  • variation of information; and
  • mutual information.

While the current focus is on supervised (a.k.a. external) performance
measures, unsupervised (internal) measures are also in scope for future
releases.

Installation

You can install the latest release from
CRAN by entering:

install.packages("clevr")

The development version can be installed from GitHub using devtools:

# install.packages("devtools")
devtools::install_github("cleanzr/clevr")

Example

Several functions are included which transform between different
clustering representations.

library(clevr)
# A clustering of four records represented as a membership vector
pred_membership <- c("Record1" = 1, "Record2" = 1, "Record3" = 1, "Record4" = 2)

# Represent as a set of record pairs that appear in the same cluster
pred_pairs <- membership_to_pairs(pred_membership)
print(pred_pairs)
#>      [,1]      [,2]     
#> [1,] "Record1" "Record2"
#> [2,] "Record1" "Record3"
#> [3,] "Record2" "Record3"

# Represent as a list of record clusters
pred_clusters <- membership_to_clusters(pred_membership)
print(pred_clusters)
#> $`1`
#> [1] "Record1" "Record2" "Record3"
#> 
#> $`2`
#> [1] "Record4"

Performance measures are available for evaluating linked pairs:

true_pairs <- rbind(c("Record1", "Record2"), c("Record3", "Record4"))

pr <- precision_pairs(true_pairs, pred_pairs)
print(pr)
#> [1] 0.3333333

re <- recall_pairs(true_pairs, pred_pairs)
print(re)
#> [1] 0.5

and for evaluating clusterings:

true_membership <- c("Record1" = 1, "Record2" = 1, "Record3" = 2, "Record4" = 2)

ari <- adj_rand_index(true_membership, pred_membership)
print(ari)
#> [1] 0

vi <- variation_info(true_membership, pred_membership)
print(vi)
#> [1] 0.8239592

Languages

R97.5%C++2.5%

Contributors

NGOLRE
GNU General Public License v2.0
Created November 17, 2020
Updated October 18, 2025