Digital Signal Processing and Artificial Intelligence for Automatic Learning Ricardo Rodríguez Jorge 5 2 2026 https://doi.org/10.6025/dspaial/2026/5/2/106-129 https://www.dline.info/dspai/fulltext/v5n2/dspaiv5n2_3.pdf Radio frequency (RF) signal datasets exhibit complex nonlinear distributions, environmental noise, and heterogeneous transmission behaviors that challenge traditional clustering algorithms. Conventional ensemble approaches often rely on static weighting strategies and overlook local structural uncertainty, limiting their robustness in dynamic wireless environments. To address these limitations, this study introduces an Adaptive Weighted Ensemble Clustering framework designed for robust latent structure discovery in RF signal datasets. The proposed methodology integrates multiple clustering algorithms, including centroidbased, density aware, hierarchical, and graph based methods, into a unified consensus architecture. By constructing a performance-driven weighted co-association matrix and incorporating local uncertainty estimation, the framework dynamically optimizes algorithmic contributions while suppressing unstable partitions. High order topological enhancement further refines neighborhood relationships, ensuring coherent subgroup formation in sparse spectral regions. Experimental evaluation demonstrates that the adaptive consensus model significantly outperforms standalone clustering baselines, achieving a consensus silhouette score of 0.722 and exhibiting exceptional reproducibility across subsampling trials. Co-association heatmap analysis reveals distinct block-diagonal structures, confirming the reliable identification of latent transmission subgroups. The framework’s dynamic adaptation capability ensures sustained performance under varying signal to noise ratios and heterogeneous dataset distributions. By bridging the gap between algorithmic instability and the stringent reliability requirements of modern RF analytics, this work establishes a scalable foundation for unsupervised spectrum intelligence, anomaly detection, and AI-driven wireless security. Future research will integrate self supervised representation learning and optimize computational efficiency for real-time edge deployment.