TW-Co-MFC: Two-level weighted collaborative fuzzy clustering based on maximum entropy for multi-view data

In recent years, multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis data in practical applications. Despite significant advances clustering, two challenges still need to be addressed, i.e., how make full use consistent and complementary information multiple views discriminate contributions different features same view efficiently reveal latent cluster structure clustering. this study, we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering (TW-Co-MFC) approach address aforementioned issues. TW-Co-MFC, two-level weighting strategy is devised measure importance features, collaborative working mechanism introduced balance within-view quality cross-view consistency. Then an iterative optimization objective function based on maximum entropy principle designed Experiments real-world datasets show effectiveness proposed approach.