Fuzzy Clustering with Self-growing Net

Wenhao Ying, Jun Wang, Zhaohong Deng, Fuquan Zhang, Zuoyong Li

Abstract: A novel deep feature mapping method self-growing net (SG-Net) is proposed, and its combination with classical fuzzy c-means (FCM) called SG-Net-FCM is further developed. SG-Net is a feedforward learning structure for nonlinear explicit feature mapping and includes four types of layers, i.e., input, fuzzy mapping, hybrid, and output layers. The fuzzy mapping layer maps the data from input layer to a high-dimensional feature space using TSK fuzzy mapping, i.e. the fuzzy mapping of Takagi–Sugeno–Kang fuzzy system (TSK-FS). Afterward, each layer in SG-Net accepts additional inputs from all preceding layers and provides its own distinguished features by using principal component analysis to all subsequent layers. The final output of SG-Net is fed to FCM. Since SG-Net-FCM is developed based on the TSK fuzzy mapping, it is more interpretable than classical kernelized fuzzy clustering methods. The effectiveness of the proposed clustering algorithm is experimentally verified on UCI datasets.