An Adaptive Hybrid Clustering Framework With Iterative Noise Filtering and a Novel DBNS Ratio Validity Measure for Outlier-Resilient High-Dimensional Data

Clustering high-dimensional data while assuring robustness to noise and outliers remains a significant challenge in unsupervised learning. This paper proposes an adaptive hybrid clustering framework that integrates the Mahalanobis distance in the latent space and iterative noise filtering to enhance...

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Detaylı Bibliyografya
Asıl Yazarlar:	Abhishek, Partha Sarathi Bishnu, Vandana Bhattacharjee
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IEEE 2026-01-01
Seri Bilgileri:	IEEE Access
Konular:	Latent space autoencoder adaptive clustering hybrid loss function Mahalanobis distance outlier detection
Online Erişim:	https://ieeexplore.ieee.org/document/11339489/
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An Adaptive Hybrid Clustering Framework With Iterative Noise Filtering and a Novel DBNS Ratio Validity Measure for Outlier-Resilient High-Dimensional Data

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