Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data
This addresses the challenge of analyzing complex, high-dimensional data with imbalanced sample representations, which is a common problem in data mining applications, though it appears to be an incremental advancement combining existing techniques.
This paper tackles the problem of mining high-dimensional imbalanced data by proposing a hierarchical framework using graph neural networks (GNNs) to capture global interdependencies and enhance minority class feature extraction, resulting in substantial improvements over traditional methods in metrics like pattern discovery count, average support, and minority class coverage.
This study presents a hierarchical mining framework for high-dimensional imbalanced data, leveraging a depth graph model to address the inherent performance limitations of conventional approaches in handling complex, high-dimensional data distributions with imbalanced sample representations. By constructing a structured graph representation of the dataset and integrating graph neural network (GNN) embeddings, the proposed method effectively captures global interdependencies among samples. Furthermore, a hierarchical strategy is employed to enhance the characterization and extraction of minority class feature patterns, thereby facilitating precise and robust imbalanced data mining. Empirical evaluations across multiple experimental scenarios validate the efficacy of the proposed approach, demonstrating substantial improvements over traditional methods in key performance metrics, including pattern discovery count, average support, and minority class coverage. Notably, the method exhibits superior capabilities in minority-class feature extraction and pattern correlation analysis. These findings underscore the potential of depth graph models, in conjunction with hierarchical mining strategies, to significantly enhance the efficiency and accuracy of imbalanced data analysis. This research contributes a novel computational framework for high-dimensional complex data processing and lays the foundation for future extensions to dynamically evolving imbalanced data and multi-modal data applications, thereby expanding the applicability of advanced data mining methodologies to more intricate analytical domains.