GTSDC: A Graph Theory Subspace-Based Analytical Algorithm for User Behavior

The exponential growth of multi-modal behavioral data in campus networks poses significant challenges for clustering analysis, including high dimensionality, redundancy, and attribute heterogeneity, which lead to degraded accuracy in existing methods. To address these issues, this study proposes a graph-theoretic subspace deep clustering framework that synergizes a deep sparse auto-encoder (DSAE) with a method of graph partitioning based on normalized cut. First, a four-layer DSAE is designed to extract discriminative features while enforcing sparsity constraints, effectively reducing data dimensionality and mitigating noise. Second, the refined subspace representations are transformed into a similarity graph, where normalized cut optimization partitions users into coherent behavioral clusters by balancing intra-cluster cohesion and inter-cluster separation. Experimental validation on three datasets—USER_DATA, MNIST, and COIL20—demonstrates the superiority of GTSDC. It achieves 91% accuracy on USER_DATA, outperforming traditional algorithms (e.g., CLIQUE, K-means) by 120% and advanced methods (e.g., deep subspace clustering) by 15%. The proposed framework not only enhances network resource allocation through behavior-aware analytics but also lays the groundwork for personalized educational services. This work bridges the gap between graph theory and deep learning, offering a scalable solution for high-dimensional behavioral pattern recognition. In simple terms, this new algorithm can more accurately analyze user behavior in campus networks. It helps universities better allocate network resources, such as ensuring smooth online classes, and can also provide personalized educational services to students according to their behavior patterns.