Package: FPDclustering 2.3.1
Cristina Tortora
FPDclustering: PD-Clustering and Related Methods
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different size. GPDC and TPDC uses a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional data sets.
Authors:
FPDclustering_2.3.1.tar.gz
FPDclustering_2.3.1.tar.gz(r-4.5-noble)FPDclustering_2.3.1.tar.gz(r-4.4-noble)
FPDclustering_2.3.1.tgz(r-4.4-emscripten)FPDclustering_2.3.1.tgz(r-4.3-emscripten)
FPDclustering.pdf |FPDclustering.html✨
FPDclustering/json (API)
| # Install 'FPDclustering' in R: |
| install.packages('FPDclustering', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- Country_data - Unsupervised Learning on Country Data
- Star - Star dataset to predict star types
- Students - Statistics 1 students
- ais - Australian institute of sport data
- asymmetric20 - Asymmetric data set shape 20
- asymmetric3 - Asymmetric data set shape 3
- outliers - Data set with outliers
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 months agofrom:8e867dfb5b. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 27 2024 |
| R-4.5-linux | OK | Aug 27 2024 |
Exports:FPDCGPDCPDCPDQSilhTPDCTuckerFactors
Dependencies:backportsbase64encbitbit64broombroom.helpersbslibcachemclassclassIntclicliprclustercolorspacecombinatcommonmarkcpp11crayondigestdplyre1071ExPositionfansifarverfastmapfontawesomeforcatsfsgenericsGGallyggeasyggplot2ggstatsgluegtablehavenhighrhmshtmltoolshttpuvisobandjquerylibjsonliteKernSmoothklaRlabelinglabelledlaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimeminiUImunsellmvtnormnlmepatchworkpillarpkgconfigplyrprettyGraphsprettyunitsprogresspromisesproxypurrrquestionrR.cacheR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppreadrrlangrootSolverprojrootrstudioapisassscalesshinysourcetoolsstringistringrstylerThreeWaytibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithrxfunxtable
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Australian institute of sport data | ais |
| Asymmetric data set shape 20 | asymmetric20 |
| Asymmetric data set shape 3 | asymmetric3 |
| Unsupervised Learning on Country Data | Country_data |
| Factor probabilistic distance clustering | FPDC |
| Gaussian PD-Clustering | GPDC |
| Data set with outliers | outliers |
| Probabilistic Distance Clustering | PDC |
| Probabilistic Distance Clustering Adjusted for Cluster Size | PDQ |
| Plots for FPDclusteringt Objects | plot.FPDclustering |
| Probabilistic silhouette plot | Silh |
| Star dataset to predict star types | Star |
| Statistics 1 students | Students |
| Summary for FPDclusteringt Objects | summary.FPDclustering |
| Student-t PD-Clustering | TPDC |
| Choice of the number of Tucker 3 factors for FPDC | TuckerFactors |