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: |
0.1.1 |
| Imports: |
checkmate, data.table, ggplot2, gridExtra, R6, iml |
| Suggests: |
testthat (≥ 3.0.0), caret, covr, knitr, mlr3, mlr3cluster, rmarkdown, FuzzyDBScan, factoextra, patchwork, spelling |
| Published: |
2024-03-25 |
| 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:
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