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fcmfd

fcmfd

Fuzzy C-Means Clustering for Ordinal Data using Triangular Fuzzy Numbers

Overview

The fcmfd package implements fuzzy clustering for ordinal Likert-type data using Triangular Fuzzy Numbers (TFNs).

It is designed for datasets where responses are measured on discrete ordinal scales (e.g., 1-5, 1–7, 1-10 or 0–10), providing a robust alternative to traditional clustering approaches.

Features

• Fuzzy C-Means clustering adapted to TFNs
• Automatic selection of the optimal number of clusters
• Xie–Beni validity index
• Support for Likert-type data
• Cluster assignment and prototype extraction
• Visualization tools

Installation

# install.packages("devtools")
devtools::install_github("yourusername/fcmfd")

Example

library(fcmfd)

# Load dataset
data(sim_likert_0_10)

# Run clustering
result <- fcmTFN(
  data = sim_likert_0_10,
  option = "B",
  k_values = 2:6
)

# Summary
summary(result)

# Cluster assignment
clusters <- cluster_assignment(result)
table(clusters)

# Plot Xie–Beni index
plot_xb(result)

Included Datasets

• sim_likert7 Simulated dataset with a 1–7 Likert scale

• sim_likert_0_10 Simulated dataset with a 0–10 Likert scale and latent cluster structure

Methodological Background

The package combines:

• Fuzzy C-Means clustering
• Triangular Fuzzy Numbers representation
• Xie–Beni cluster validity index

to provide a framework tailored for ordinal data.

Uses Cases

• Survey analysis
• Social sciences
• Customer satisfaction
• Quality of life studies
• Likert-type data clustering

References

Coppi, R., D’Urso, P., & Giordani, P. (2011). Fuzzy clustering of fuzzy data. Computational Statistics & Data Analysis. https://doi.org/10.1016/j.csda.2010.09.013

Xie, X. L., & Beni, G. (1991). A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.85677

Author

José Ortigas

License

MIT

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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