The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for various real-world purposes, such as industrial quality inspection, ...
Abstract: Density-based clustering algorithms are widely used for discovering clusters of arbitrary shapes without prior knowledge, yet they encounter major challenges when dealing with fuzzy ...
HypeFCM is a fuzzy clustering algorithm for non-Euclidean spaces, combining hyperbolic geometry with adaptive weight-based filtering in the Poincaré Disc model. It efficiently captures hierarchical ...
HypeFCM is a fuzzy clustering algorithm for non-Euclidean spaces, combining hyperbolic geometry with adaptive weight-based filtering in the Poincaré Disc model. It efficiently captures hierarchical ...
Abstract: Soft clustering algorithms based on fuzzy C-means (FCM) have been extensively applied to complex data analysis. However, existing FCM variants still encounter key limitations: a large number ...
ABSTRACT: Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering ...
Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering algorithms to a ...
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