  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 between sample points, thus improving the
algorithm's performance and robustness. The paper also establishes theoretical
connections between the proposed model and popular Fuzzy K-Means clustering
techniques. Experimental results on several real datasets demonstrate the
effectiveness of the algorithm.
