The R Journal: article published in 2019, volume 11:1

fclust: An R Package for Fuzzy Clustering
Maria Brigida Ferraro, Paolo Giordani and Alessio Serafini , The R Journal (2019) 11:1, pages 198-210.

Abstract Fuzzy clustering methods discover fuzzy partitions where observations can be softly assigned to more than one cluster. The package fclust is a toolbox for fuzzy clustering in the R programming language. It not only implements the widely used fuzzy k-means (FkM) algorithm, but also many FkM variants. Fuzzy cluster similarity measures, cluster validity indices and cluster visualization tools are also offered. In the current version, all the functions are rewritten in the C++ language allowing their application in large-size problems. Moreover, new fuzzy relational clustering algorithms for partitioning qualitative/mixed data are provided together with an improved version of the so-called Gustafson-Kessel algorithm to avoid singularity in the cluster covariance matrices. Finally, it is now possible to automatically select the number of clusters by means of the available fuzzy cluster validity indices.

Received: 2018-12-01; online 2019-08-15, supplementary material, (2.5 KiB)
CRAN packages: fclust, cluster, clue, e1071, skmeans, vegclust, ppclust, Rcpp, RcppArmadillo, smacof, MASS
CRAN Task Views implied by cited CRAN packages: Cluster, Multivariate, Environmetrics, NumericalMathematics, Psychometrics, Distributions, Robust, Econometrics, HighPerformanceComputing, MachineLearning, NaturalLanguageProcessing, Optimization, SocialSciences, TeachingStatistics

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-017,
  author = {Maria Brigida Ferraro and Paolo Giordani and Alessio
          Serafini},
  title = {{fclust: An R Package for Fuzzy Clustering}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-017},
  url = {https://doi.org/10.32614/RJ-2019-017},
  pages = {198--210},
  volume = {11},
  number = {1}
}