Clustering Ensemble Based on Fuzzy Matrix Self-Enhancement
Xia Ji, Jiawei Sun, Jianhua Peng, Yue Pang, Peng Zhou
Abstract
Fuzzy clustering ensemble techniques have been proven to yield more accurate and robust clustering results, with the mainstream methods relying on the fuzzy co-association (FCA) matrix. However, the inherent issues of low-value density and uniform dispersion in the FCA matrix significantly affect the performance of fuzzy clustering ensembles, an aspect that has been overlooked. To address this issue, we propose a novel framework for fuzzy clustering ensemble based on fuzzy matrix self-enhancement (FMSE). Specifically, we initially employ singular value decomposition to extract the principal components of the FCA matrix, thereby alleviating its low-value density. Second, on the basis of the criterion of fuzzy entropy, we measure the fuzziness of samples, design a metric for the fuzzy representativeness of samples, and incorporate it into a fusion-weighted structure for the reconstruction of the FCA matrix, mitigating uniform dispersion. Subsequently, on the basis of the self-enhanced fuzzy matrix model, we utilize a prototype diffusion approach to identify core samples and gradually allocate remaining samples to obtain a consensus clustering solution. Extensive comparative experiments on benchmark datasets against state-of-the-art clustering ensemble methods demonstrate the effectiveness and superiority of the proposed approach.