Adaptive Ensemble Clustering With Boosting BLS-Based Autoencoder

Ensemble clustering has an advantage in producing a more promising and robust clustering result by combining multiple partitions strategically. The quality of both base partitions and co-association matrix plays an essential role in improving the consensus partition. However, the current ensemble clustering methods have several limitations: 1) The noise in high-dimensional feature space is ignored; 2) The independent base partition generation process does not pay attention to ambiguous samples; 3) The co-association matrix and the weights of base partitions commonly lack of theoretical optimization. In order to address these issues, we propose an adaptive ensemble clustering framework with boosting BLS-based autoencoder (BoostAEC). In the generation step, a boosting BLS-based autoencoder (BoostBLSAE) is designed to generate base partitions sequentially, which learns compressed feature subspaces for ambiguous samples and adaptively evaluates the corresponding weights of reliability. In the integration step, we construct a fuzzy membership function to capture the inter-cluster correlation and explicitly propose a consensus objective function to optimize the unified co-association matrix by considering the weighted base partitions. Extensive experiments on various real-world datasets demonstrate the superior performance of BoostAEC to the state-of-the-art ensemble clustering methods.