A Multiclustering Evolutionary Hyperrectangle-Based Algorithm

Abstract

The proliferation of high-dimensional datasets across critical infrastructures necessitates the development of clustering methodologies that are not only computationally efficient but also structurally interpretable. Traditional centroid-based or density-based clustering techniques often struggle with the "curse of dimensionality" and the inherent noise present in large-scale socio-technical systems. This paper proposes a systemic architecture for a Multiclustering Evolutionary Hyperrectangle-Based Algorithm (MEHBA), designed to optimize data partitioning through the evolutionary refinement of axis-aligned hyperrectangles. By utilizing a multiclustering approach, the algorithm allows for the simultaneous discovery of overlapping and hierarchical structures within complex datasets, providing a more granular representation of system states. We provide a deep analytical discussion on the structural trade-offs between geometric flexibility, computational latency, and the robustness of the evolutionary search process. The research emphasizes the importance of hyperrectangle-based representations in enhancing the transparency of machine learning models, particularly in domains such as smart grid management, precision agriculture, and financial infrastructure. Furthermore, the paper explores the socio-technical dimensions of deploying such algorithms, addressing issues of algorithmic governance, the sustainability of high-compute evolutionary processes, and the policy implications of automated decision-making. Our findings suggest that the MEHBA framework offers a resilient and interpretable alternative to black-box clustering methods, facilitating a more accountable and sustainable integration of artificial intelligence into large-scale engineering systems.