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title: "fcmfd"
output: github_document
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<!-- README.md is generated from README.Rmd. Please edit that file -->

# fcmfd

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

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## 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.

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## 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

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## Installation

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

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## Example

```r
library(fcmfd)

# Load dataset
data(sim_likert7)

# Run clustering
result <- fcmTFN(
  data = sim_likert7,
  option = "B",
  k_values = 2:6
)

# Summary
summary(result)

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

# Plot Xie–Beni index
plot_xb(result)
```

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## Included Datasets

* **sim_likert7**
  Simulated dataset with a 1–7 Likert scale

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## 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.

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## Uses Cases

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

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## 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>

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## Author

José Ortigas

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## License

MIT
