Robust Trimmed Clustering.
tclust: Robust Trimmed Clustering
tclust provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) doi:10.1214/07-AOS515, Fritz et al. (2012) doi:10.18637/jss.v047.i12, Garcia-Escudero et al. (2011) doi:10.1007/s11222-010-9194-z and others.
tclust package is on CRAN (The Comprehensive R Archive Network) and the latest release can be easily installed using the command
Building from source
To install the latest stable development version from GitHub, you can pull this repository and install it using
## install.packages("remotes") remotes::install_github("valentint/tclust", build_opts = c("--no-build-vignettes"))
Of course, if you have already installed
remotes, you can skip the first line (I have commented it out).
Outlying data can heavily influence standard clustering methods. At the same time, clustering principles can be useful when robustifying statistical procedures. These two reasons motivate the development of feasible robust model-based clustering approaches. Instead of trying to “fit” noisy data, a proportion α of the most outlying observations is trimmed. The
tclust package efficiently handles different cluster scatter constraints. Graphical exploratory tools are also provided to help the user make sensible choices for the trimming proportion as well as the number of clusters to search for.
library(tclust) data (M5data) x <- M5data[, 1:2] clus.a <- tclust (x, k = 3, alpha = 0.1, restr.fact = 1, restr = "eigen", equal.weights = TRUE, warnings = 1) clus.b <- tclust (x, k = 3, alpha = 0.1, restr.fact = 1, equal.weights = TRUE, warnings = 1) clus.c <- tclust (x, k = 3, alpha = 0.1, restr.fact = 1, restr = "deter", equal.weights = TRUE, iter.max = 100, warnings = 1) clus.d <- tclust (x, k = 3, alpha = 0.1, restr.fact = 50, restr = "eigen", equal.weights = FALSE) pa <- par (mfrow = c (2, 2)) plot (clus.a, main = "(a) tkmeans") plot (clus.b, main = "(b) Gallegos and Ritter") plot (clus.c, main = "(c) Gallegos") plot (clus.d, main = "(d) tclust")
The trimmed k-means clustering method by Cuesta-Albertos, Gordaliza and Matran (1997) optimizes the k-means criterion under trimming a portion of the points:
library(tclust) data (swissbank) ## Two clusters and 8% trimming level clus <- tkmeans (swissbank, k = 2, alpha = 0.08) ## Pairs plot of the clustering solution pairs (swissbank, col = clus$cluster + 1)
# Two coordinates plot (swissbank[, 4], swissbank[, 6], col = clus$cluster + 1, xlab = "Distance of the inner frame to lower border", ylab = "Length of the diagonal")
Report issues and request features
If you experience any bugs or issues or if you have any suggestions for additional features, please submit an issue via the Issues tab of this repository. Please have a look at existing issues first to see if your problem or feature request has already been discussed.
Contribute to the package
If you want to contribute to the package, you can fork this repository and create a pull request after implementing the desired functionality.
Ask for help
If you need help using the package, or if you are interested in collaborations related to this project, please get in touch with the package maintainer.