This paper presents a unified spectral clustering framework enhanced by chordal graph decomposition to address large-scale network clustering challenges intrinsic to AI and Data Science. By preserving the essential spectral characteristics of the original network Laplacian through chordal subgraph extraction, the method efficiently reduces computational complexity without sacrificing clustering accuracy. The chordal decomposition leverages maximum cardinality search and clique-tree construction to organize vertices hierarchically, thereby accelerating spectral embedding and improving cluster interpretability. The framework exhibits near-linear scalability on sparse large-scale networks, making it suitable for diverse AI&DS applications such as social network analysis, recommendation systems, and semi-supervised learning on graph-structured data. Benchmark evaluations demonstrate significant improvements in cluster quality and computational efficiency compared to classical spectral clustering baselines. This approach offers a robust, scalable, and interpretable tool for discovering latent structures in complex datasets, thereby advancing network-centric analytics in AI and Data Science.

Keywords: spectral clustering, chordal decomposition, network analysis, AI-driven clustering