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September 5, 2025

Enhanced Features extraction methodbased on Fuzzy C-meansalgorithm

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Authors

LMLamia AbedNoor MuhammedNHNidhal Hasan Hasaan

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Overview

This work demonstrates enhanced classification performance using fuzzy c-means clustering, suggesting that membership values improve feature quality.

Key Points

  • The proposed method significantly improved classification accuracy across multiple datasets, enhancing feature extraction.
  • Using fuzzy c-means, the algorithm achieved an accuracy of 99.10% on the CICIDS2017 dataset, demonstrating its effectiveness.
  • An observational analysis compared fuzzy c-means with K-means for feature extraction, evaluating results through the KNN algorithm.
  • This approach suggests that fuzzy c-means clustering is better suited for complex data with unclear boundaries, offering robust feature generation.

Cite This Study

Muhammed et al. (2025) studied this question.

synapsesocial.com/papers/68bb3d682b87ece8dc956acbhttps://doi.org/10.31237/osf.io/cy7dz_v1
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