Clustering ensemble improves clustering quality by integrating multiple base clustering results; however, existing methods suffer from inadequate handling of boundary uncertainty and lack a unified probabilistic-to-decision framework. This paper proposes GMM-3WD-CE, which integrates Gaussian Mixture Model (GMM) with three-way decision (3WD) theory to construct a multi-level uncertainty modelling framework. The method generates \(M=50\) diverse base clusterings via a multi-algorithm strategy, constructs a weighted co-association matrix using quality scores derived from the silhouette coefficient, the Caliński–Harabasz index, and the Davies–Bouldin index, employs the ICL criterion for optimal GMM model selection, and adaptively calculates three-way decision thresholds through the Otsu algorithm to partition samples into core, boundary, and trivial domains. Differentiated label-assignment strategies for each region yield the final consensus clustering. Comparative experiments on eight benchmark datasets with nine comparison methods show that GMM-3WD-CE achieves statistically significant average improvements of \(3.2\%\) in NMI and \(3.9\%\) in ARI over PCPA and \(8.8\%\) in NMI and \(10.4\%\) in ARI over classical MCLA, while remaining competitive with the strongest recent baseline, SDGCA ( \(1.2\%\) average NMI advantage; Wilcoxon \(p=0.089\) , medium effect size \(d=0.41\) ). Ablation experiments verify the contribution of each component; Wilcoxon and Friedman tests with Cohen’s d effect sizes confirm statistical significance against all other baselines; and runtime/scalability analyses characterise the computational trade-offs.
