Abstract：Aiming at the clustering of sample datasets with small inter-class distance and poor separability, an adaptive Rulkov neuron clustering algorithm is proposed. Firstly, a similarity matrix based on adaptive distance and shared nearest neighbor is constructed. Secondly, the optimal segmentation of the undirected graph consisting of samples is replaced by the Laplace spectral decomposition of the matrix according to the similarity matrix, and the eigen vectors of Laplacian matrix with larger eigen values are selected as new features of the samples. Thus, the inter-class distance is increased and the intra-class spacing of the samples is reduced. Then, the samples are mapped to the neurons with the mutual coupling strength determined by the distance of the samples. The separability of the different clusters is improved by the self-learning of the mutual coupling strength. Finally, the strong coupled subset in the neural network is utilized as clustering result. The comparative experiments are conducted on synthetic and real datasets. The results show that the proposed algorithm achieves better clustering performance.
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