Package: arc 1.4.1
arc: Association Rule Classification
Implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>, contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the 'arules' package. To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 'qCBA' package is suggested.
Authors:
arc_1.4.1.tar.gz
arc_1.4.1.zip(r-4.5)arc_1.4.1.zip(r-4.4)arc_1.4.1.zip(r-4.3)
arc_1.4.1.tgz(r-4.4-any)arc_1.4.1.tgz(r-4.3-any)
arc_1.4.1.tar.gz(r-4.5-noble)arc_1.4.1.tar.gz(r-4.4-noble)
arc_1.4.1.tgz(r-4.4-emscripten)arc_1.4.1.tgz(r-4.3-emscripten)
arc.pdf |arc.html✨
arc/json (API)
NEWS
| # Install 'arc' in R: |
| install.packages('arc', repos = c('https://kliegr.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kliegr/arc/issues
- humtemp - Comfort level based on temperature and humidity of the environment
Last updated 10 days agofrom:dc414aa281. Checks:OK: 5 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 08 2024 |
| R-4.5-win | NOTE | Sep 08 2024 |
| R-4.5-linux | NOTE | Sep 08 2024 |
| R-4.4-win | OK | Sep 08 2024 |
| R-4.4-mac | OK | Sep 08 2024 |
| R-4.3-win | OK | Sep 08 2024 |
| R-4.3-mac | OK | Sep 08 2024 |
Exports:applyCutapplyCutscbacba_manualcbaCSVcbaIriscbaIrisNumericCBARuleModelCBARuleModelAccuracydiscretizeUnsuperviseddiscrNumericgetAppearancegetConfVectorForROCmdlp2prunetopRules
Dependencies:arulesdiscretizationgenericslatticeMatrixR.methodsS3R.ooR.utils
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Apply Cut Points to Vector | applyCut |
| Apply Cut Points to Data Frame | applyCuts |
| CBA Classifier | cba |
| CBA Classifier from provided rules | cba_manual |
| Example CBA Workflow with CSV Input | cbaCSV |
| Test CBA Workflow on Iris Dataset | cbaIris |
| Test CBA Workflow on Iris Dataset with numeric target | cbaIrisNumeric |
| CBARuleModel | CBARuleModel CBARuleModel-class |
| Prediction Accuracy | CBARuleModelAccuracy |
| Unsupervised Discretization | discretizeUnsupervised |
| Discretize Numeric Columns In Data frame | discrNumeric |
| Method that generates items for values in given data frame column. | getAppearance |
| Returns vector with confidences for the positive class (useful for ROC or AUC computation) | getConfVectorForROC |
| Comfort level based on temperature and humidity of the environment | humtemp |
| Supervised Discretization | mdlp2 |
| Apply Rule Model | predict.CBARuleModel |
| Classifier Builder | prune |
| Rule Generation | topRules |
