Association and Consolidation: Evolutionary Memory-Enhanced Incremental Multi-View Clustering

The introduction of Evolutionary Memory-Enhanced Incremental Multi-View Clustering (EMIMC) addresses the critical stability-plasticity dilemma faced in incremental multi-view clustering scenarios. This innovative method incorporates three key modules: a rapid association module that connects new and historical views, a cognitive forgetting module that optimizes knowledge integration through a decay mechanism, and a knowledge consolidation module that refines short-term knowledge into stable long-term memory. Extensive experiments have demonstrated that EMIMC significantly outperforms existing state-of-the-art methods, marking a substantial advancement in artificial intelligence. By mimicking the memory regulation mechanisms of the human brain, EMIMC not only enhances the model's ability to adapt to new data but also ensures the retention of long-term knowledge, thereby providing a robust solution to the challenges of incremental learning.