Fuzzy K-Means Clustering without Cluster Centroids

arXiv:2404.04940v1 Announce Type: new
Abstract: Fuzzy K-Means clustering is a critical technique in unsupervised data analysis. However, the performance of popular Fuzzy K-Means algorithms is sensitive to the selection of initial cluster centroids and is also affected by noise when updating mean cluster centroids. To address these challenges, this paper proposes a novel Fuzzy K-Means clustering algorithm that entirely eliminates the reliance on cluster centroids, obtaining membership matrices solely through distance matrix computation. This innovation enhances flexibility in distance measurement …

abstract algorithms analysis arxiv challenges cluster clustering cs.lg data data analysis however k-means mean noise novel paper performance popular type unsupervised