Fuzzy Unsupervised and Semi-Supervised Clustering

Methods for distance-based fuzzy unsupervised and semi-supervised clustering, including fuzzy and possibilistic models based on alternating optimization (AO) algorithm. The package introduces a vectorized estimation framework for prototype-based fuzzy clustering algorithms, enabling modular algorithm design and extensibility. It also supports storage and retrieval of intermediate AO optimization results for downstream analysis and processing. For more details see Kmita et al. (2024) .

fussclust

The goal of fussclust is to provide methods for fuzzy unsupervised and semi-supervised clustering, including fuzzy, possibilistic, and hybrid models.

Installation

You can install the development version of fussclust from GitHub with:

# install.packages("devtools")
devtools::install_github("kkmita/fussclust")

Example

This is a basic example which shows how to fit unsupervised and semi-supervised models.

library(fussclust)

X <- matrix(rnorm(100), ncol = 2)

model_fcm <- fussclust::FCM(X = X, C = 2)
model_pcm <- fussclust::PCM(X = X, C = 2)

superF <- matrix(0, nrow = nrow(X), ncol = ncol(X))
superF[1:10, 1] <- 1
superF[20:30, 2] <- 1

model_ssfcm <- fussclust::SSFCM(X = X, C = 2, superF = superF, alpha = 1)
model_sspcm <- fussclust::SSPCM(X = X, C = 2, superF = superF, alpha = 1)