\(SC^2\): A Selection-Based Consensus Clustering Approach

Consensus clustering, also called clustering ensemble, is a method of improving quality and robustness in clustering by optimally combining an ensemble of clusterings generated in different ways. In this work, we introduce our approach that is based on a selection-based model and use cumulative voting strategy in order to arrive at a consensus . We demonstrate the performance of our proposed method on several benchmark datasets and show empirically that it outperforms some well-known consensus clustering algorithms.