FACT: Feature Attributions for ClusTering

We present 'FACT' (Feature Attributions for ClusTering), a framework for unsupervised interpretation methods that can be used with an arbitrary clustering algorithm. The package is capable of re-assigning instances to clusters (algorithm agnostic), preserves the integrity of the data and does not introduce additional models. 'FACT' is inspired by the principles of model-agnostic interpretation in supervised learning. Therefore, some of the methods presented are based on 'iml', a R Package for Interpretable Machine Learning by Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl (2018) <doi:10.21105/joss.00786>.

Version: 0.1.0
Imports: checkmate, data.table, ggplot2, gridExtra, prediction, R6, iml
Suggests: testthat (≥ 3.0.0), caret, covr, knitr, mlr3, mlr3cluster, rmarkdown, FuzzyDBScan, factoextra, patchwork, spelling
Published: 2023-03-16
Author: Henri Funk [aut, cre], Christian Scholbeck [aut, ctb], Giuseppe Casalicchio [aut, ctb]
Maintainer: Henri Funk <Henri.Funk at stat.uni-muenchen.de>
BugReports: https://github.com/henrifnk/FACT/issues
License: LGPL-3
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: FACT results

Documentation:

Reference manual: FACT.pdf

Downloads:

Package source: FACT_0.1.0.tar.gz
Windows binaries: r-devel: FACT_0.1.0.zip, r-release: FACT_0.1.0.zip, r-oldrel: FACT_0.1.0.zip
macOS binaries: r-release (arm64): FACT_0.1.0.tgz, r-oldrel (arm64): FACT_0.1.0.tgz, r-release (x86_64): FACT_0.1.0.tgz

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