Adaptive tree-reinforced clustering using hierarchical social relations and Q-learning in dynamic data environments

Efficient clustering in large-scale, heterogeneous, and evolving data systems remains a core challenge in data-driven intelligence. Traditional clustering models often face limitations related to computational complexity, noise vulnerability, and lack of adaptability. To overcome these issues, this study introduces the Adaptive Tree–Reinforced Clustering (ATRC) framework, a hybrid model that fuses the Tree Social Relation (TSR) mechanism with Q-Learning-based reinforcement optimization. In this model, TSR establishes hierarchical connections among data entities to facilitate the rapid creation of preliminary clusters, while Q-Learning dynamically refines cluster assignments through iterative state–action evaluation to avoid local minima. The cooperative interaction between TSR’s structural organization and Q-Learning’s adaptive exploration enhances both convergence stability and accuracy, particularly in uncertain or noisy conditions. Experimental results on a heterogeneous IoT sensor network with 32 nodes and four adaptive clusters demonstrate that ATRC achieves superior performance over LSCC-RL, CRL-AC, and CRAQL benchmarks yielding up to 13% higher packet delivery rates, 28% lower energy consumption, and nearly 0.9 × faster convergence. These results confirm that integrating reinforcement-based learning with hierarchical modeling yields a scalable and energy-efficient clustering framework suitable for real-time IoT, fog computing, and data-intensive intelligent systems.