IRFCA: A Hybrid Fuzzy-Possibilistic Clustering Algorithm for High-Dimensional Data

Sumrana Siddiqui; Nandita Bhanja Chaudhuri

doi:10.48084/etasr.16837

Authors

Sumrana Siddiqui Computer Science Engineering Department, GITAM School of Technology, India
Nandita Bhanja Chaudhuri Computer Science Engineering Department, GITAM School of Technology, India

Volume: 16 | Issue: 2 | Pages: 33065-33071 | April 2026 | https://doi.org/10.48084/etasr.16837

Received: 11 December 2025 | Revised: 29 December 2025, 13 January 2026, and 25 January 2026 | Accepted: 27 January 2026 | Online: 1 March 2026

Corresponding author: Nandita Bhanja Chaudhuri

Abstract

Clustering high-dimensional datasets poses significant challenges due to the "curse of dimensionality," noise, and outliers, which render traditional algorithms like FCM and K-Means ineffective. The primary objective of this study was to develop a robust framework that balances noise resilience with the scalability required for large-scale data. The proposed Intelligent Robust Fuzzy Clustering Algorithm (IRFCA) is a hybrid pipeline that integrates Truncated SVD for dimensionality reduction, K-Means++ for stable initialization, and a fused FCM-PCM mechanism with dynamic outlier trimming. Tested on large-scale datasets including Network Attack Data (10.9 GB) and CORD-19, IRFCA demonstrates superior clustering quality, achieving Silhouette scores up to 0.63 and Davies-Bouldin Indices as low as 0.34, significantly outperforming baselines (FCM, FCMedian, PCM). These results quantify IRFCA's ability to produce compact, well-separated clusters while maintaining competitive scalability. IRFCA offers a resilient solution for high-dimensional analysis, with transformative potential for cybersecurity, business analytics, and scientific research.

Keywords:

hybrid clustering, high-dimensional data, Principal Component Analysis (PCA), fuzzy clustering, Possibilistic C-Means (PCM), outlier trimming

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