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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-017.zip
fclust, cluster, clue, e1071, skmeans, vegclust, ppclust, Rcpp, RcppArmadillo, smacof, MASS
Cluster, Multivariate, Environmetrics, NumericalMathematics, Psychometrics, Distributions, Robust, Econometrics, HighPerformanceComputing, MachineLearning, NaturalLanguageProcessing, Optimization, SocialSciences, TeachingStatistics
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For attribution, please cite this work as
Ferraro, et al., "fclust: An R Package for Fuzzy Clustering", The R Journal, 2019
BibTeX citation
@article{RJ-2019-017,
author = {Ferraro, Maria Brigida and Giordani, Paolo and Serafini, Alessio},
title = {fclust: An R Package for Fuzzy Clustering},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-017},
doi = {10.32614/RJ-2019-017},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {198-210}
}