Deep Streaming View Clustering

Existing deep multi-view clustering methods have demonstrated excellent performance, which addressing issues such as missing views and view noise. However, almost all existing methods are within a static framework, which assumes that all views have already been collected. Nevertheless, in practical scenarios, new views are continuously collected over time, which forms the stream of views. Additionally, there exists the data imbalance of distribution between different view streams, i.e., concept drift problem. To this end, we propose a novel Deep Streaming View Clustering (DSVC) method, which mitigates the impact of concept drift on streaming view clustering (SVC). Specifically, DSVC consists of a knowledge base and three core modules. Through the knowledge aggregation learning module, DSVC extracts representative features and prototype knowledge from the new view. Subsequently, the distribution consistency learning module aligns the prototype knowledge from the current view with the historical knowledge distribution to mitigate the impact of concept drift. Then, the knowledge guidance learning module leverages the prototype knowledge to guide the data distribution and enhance the clustering structure. Finally, the prototype knowledge from the current view is updated in the knowledge base to guide the learning of subsequent views. Extensive experiments demonstrate that DSVC significantly outperforms state-of-the-art methods.