Fuzzy self-consistent clustering ensemble

Abstract

The subject of clustering ensemble (CE) has emerged as a pivotal area of exploration within unsupervised learning, primarily due to its remarkable capacity to integrate multiple base clusterings. However, the conventional CE framework exhibits limitations in effectively handling fuzzy relations and ensuring robustness. This study introduces an innovative and robust fuzzy self-consistent clustering ensemble (FSCE) model. Departing from customary approaches to processing base clustering outcomes, the FSCE model considers the scalable dummy variable representation of base clustering results as a novel feature attributes intrinsic to the original dataset. Subsequently, a γ-fuzzy operator ε+ is formulated, enabling the adjustment of coupling strength contingent upon the uncertainties inherent in practical problems. Leveraging this operator, the model reappraises the fuzzy relationships among objects, thereby engendering a corresponding relation matrix. This matrix profoundly enhances the model's capacity to contend with uncertain relationships. The resultant relation matrix assumes a pivotal role as the primary input in the consensus function, culminating in the derivation of the ensemble outcome. Moreover, the FSCE model deviates from the conventional CE paradigm by introducing a reallocation strategy for fuzzy objects within ensemble outcome. Through comprehensive experimentation, we substantiate that the introduction of γ-fuzzy operator ε+ offers a viable novel approach to enhancing both the overall performance and the fuzzy interpretability of the CE model. The FSCE model distinctly excels in consensus formation and robustness when juxtaposed against eight archetypal and state-of-the-art clustering models.