A Novel Feature-Scheduling Aggregation Clustering Framework Based on Convolutional Neural Networks

This study presents a convolutional neural network (CNN)-based feature-scheduling aggregation clustering framework designed to address the limitations of conventional clustering algorithms in handling high-dimensional data structures. The proposed framework synergistically combines CNN’s automated feature extraction with adaptive feature-scheduling mechanisms and multi-level feature aggregation, enabling the effective capture of nonlinear data relationships and fine-grained pattern distinctions. The experimental results highlight the framework’s superior performance against existing algorithms, alongside its strong generalization capability. This framework advances cluster analysis methodology by providing an integrated deep learning architecture for complex data scenarios.