Weighted adaptively ensemble clustering method based on fuzzy Co-association matrix

Abstract

Although many existing co-association (CA) matrix-based ensemble clustering methods have witnessed their superiority in unsupervised learning, but these methods still suffer the following problems: (1) the CA matrix may only focus on strengthening the co-association relations of the pairwise samples in the same cluster and ignoring other information (e.g., inter-cluster association relationship). (2) the CA matrix should be directly used to obtain the ensemble clustering result without using other clustering methods (e.g., spectral clustering). (3). the weights of each base clustering result should be assigned adaptively to reflect the importance of each base clustering result. In order to circumvent the above problems, we propose a novel weighted adaptively ensemble clustering method based on fuzzy co-association matrix (called WEC-FCA) in our study. Concretely, the proposed method WEC-FCA first proposes a novel CA matrix called fuzzy co-association (FCA) matrix to reflect both the co-association relations and inter-cluster association relationships of the pairwise samples in dataset. Then by introducing the rank constraint into the FCA matrix, a novel optimalization framework is invented to obtain the optimal ensemble FCA matrix having the same total number of components as the true number of clusters, so as to be directly used to obtain the ensemble clustering result. Besides, the weights of each base clustering result can be assigned adaptively during the optimization process by using the Shannon entropy. Experimental results on both synthetic and real datasets confirm that the proposed method WEC-FCA keeps at least comparable clustering performance to the state-of-the-art ensemble clustering methods.