Package: FPDclustering 2.3.2
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 center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use 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_2.3.2.tar.gz
FPDclustering_2.3.2.tar.gz(r-4.5-noble)FPDclustering_2.3.2.tar.gz(r-4.4-noble)
FPDclustering_2.3.2.tgz(r-4.4-emscripten)FPDclustering_2.3.2.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 7 days agofrom:a43b7e147d. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 12 2024 |
R-4.5-linux | OK | Dec 12 2024 |
Exports:FPDCGPDCPDCPDQSilhTPDCTuckerFactors
Dependencies:base64encbitbit64bslibcachemclassclassIntclicliprclustercolorspacecombinatcommonmarkcpp11crayondigestdplyre1071ExPositionfansifarverfastmapfontawesomeforcatsfsgenericsGGallyggeasyggplot2ggstatsgluegtablehavenhighrhmshtmltoolshttpuvisobandjquerylibjsonliteKernSmoothklaRlabelinglabelledlaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimeminiUImunsellmvtnormnlmepatchworkpillarpkgconfigplyrprettyGraphsprettyunitsprogresspromisesproxypurrrquestionrR.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 |