Fuzzy K-Means Clustering without Cluster Centroids
This addresses a specific bottleneck in unsupervised data analysis for researchers and practitioners by offering a more robust clustering method, though it is incremental as it builds on existing Fuzzy K-Means techniques.
The paper tackled the sensitivity of Fuzzy K-Means clustering to initial centroids and noise by proposing a novel algorithm that eliminates cluster centroids, using only distance matrix computations for membership metrics, which improved performance and robustness as shown in experiments on real datasets.
Fuzzy K-Means clustering is a critical technique in unsupervised data analysis. Unlike traditional hard clustering algorithms such as K-Means, it allows data points to belong to multiple clusters with varying degrees of membership, determined through iterative optimization to establish optimal cluster centers and memberships, thereby achieving fuzzy partitioning of data. 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 \textit{K}-Means clustering algorithm that entirely eliminates the reliance on cluster centroids, obtaining membership metrics 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.