Robust denoising FCM clustering via L2,1 NMF and local constraint

The Fuzzy C-Means (FCM) algorithm is widely used in data mining and machine learning. However,the sensitivity of FCM to the initial value and noise inevitably leads to the decline of the clusteringeffect. In this paper, a new improved fuzzy clustering algorithm is proposed— Robust denoising FCMclustering via L2,1 NMF and local constraint (RFCM-L 2,1NMF). Firstly, RFCM-L 2,1NMF combinesthe L 2,1NMF that has noise residual estimation with FCM, using the robustness and noise constraintterms of the L 2,1NMF to attenuate the effect of noise on data clustering. Secondly, RFCM-L 2,1NMFuses the low-dimensional representation of L 2,1NMF as the initial value of FCM, which reduces thedefects of FCM caused by the initial value to a certain extent, and makes the clustering effect morestable. Furthermore, since the low-dimensional representation of L 2,1NMF is the hub connecting L 2,1NMF and FCM, to obtain a more accurate low-dimensional representation, we construct a newlocal constraint term in this paper. Finally, experiments on data sets validate that RFCM-L 2,1NMFis superior compared to other state-of-the-art methods.