Structured Graph-Based Ensemble Clustering

Ensemble clustering can utilize the complementary information among multiple base clusterings, and obtain a clustering model with better performance and more robustness. Despite its great success, there are still two problems in the current ensemble clustering methods. First, most ensemble clustering methods often treat all base clusterings equally. Second, the final ensemble clustering result often relies on $k$-means or other discretization procedures to uncover the clustering indicators, thus obtaining unsatisfactory results. To address these issues, we proposed a novel ensemble clustering method based on structured graph learning, which can directly extract clustering indicators from the obtained similarity matrix. Moreover, our methods take sufficient consideration of correlation among the base clusterings and can effectively reduce the redundancy among them. Extensive experiments on artificial and real-world datasets demonstrate the efficiency and effectiveness of our methods.