A Stochastic Agnostic Meta‐Clustering Framework: Application to African Agro‐Ecological Zoning

ABSTRACT

This article introduces Stochastic Agnostic Meta‐Clustering (SAMC), a novel ensemble clustering framework designed to address the challenges of high‐dimensional and uncertain data. SAMC integrates stochastic feature subspace decomposition with metaheuristic optimization to support both hard and soft clustering, making it well suited for domains characterized by gradual transitions and fuzzy boundaries, such as environmental and agricultural systems. The proposed framework is evaluated on an agro‐ecological zoning (AEZ) task using data structured around SCORPAN factors and is compared against established hard and soft clustering methods, with and without dimensionality reduction. Experimental results demonstrate the SAMC's advantageous trade‐off between interpretability and scalability while improving robustness to feature heterogeneity and preventing over‐fragmentation. In addition, post‐clustering interpretability analyses are conducted to identify dominant agro‐ecological drivers and representative rules characterizing the resulting zones. The comparative analysis further confirms that soft clustering approaches are better suited to capturing the fuzzy boundaries inherent in environmental systems and provided a soft clustering‐based AEZ analysis at the continental scale for Africa.