Towards Balance Adaptive Weighted Ensemble Clustering

Ensemble clustering, which combines the information from multiple base clusterings to obtain a better partition result, has received extensive attention due to its effectiveness and robustness. Although many algorithms have been developed in recent years that have achieved impressive results in practical applications, two challenging issues in ensemble clustering remain. First, most algorithms assume that all base clusterings have the same impact on the clustering results, assigning them the same weight. This makes the clustering performance susceptible to the influence of redundant, low-quality base clusterings. Second, co-association matrix-based algorithms often rely on additional methods, such as hierarchical agglomerative clustering, to obtain the final clustering result after constructing the weighted co-association matrix. This not only complicates optimization process but also leads to the loss of some sample-similarity information during clustering. To address this problem, we propose a novel Towards Balance Adaptive Weighted Ensemble Clustering (TBAWEC) algorithm. This method transforms the ensemble clustering problem into an optimization problem, producing the final result without requiring additional clustering algorithms. Moreover, we introduce balanced technology into ensemble clustering for the first time, significantly improving the balance of clustering results. Extensive experiments on real datasets demonstrate that the proposed algorithm outperforms the most advanced ensemble and balanced clustering algorithms simultaneously.