自适应Rulkov神经元聚类算法

针对类间间距较小、可分性较差的样本数据聚类问题,文中提出自适应Rulkov神经元聚类算法.首先,构建基于自适应距离和共享近邻的相似度矩阵,将样本构成的无向图的最优分割问题转化为拉普拉斯矩阵的谱分解问题,并按特征值大小选取拉普拉斯矩阵的特征向量作为新的样本特征,增大样本类间间距,减小类内间距.然后,将样本根据新特征映射为神经元,样本特征距离决定神经元之间的耦合权值,通过耦合强度自学习进一步提升样本可分性.最后,通过强连通分量实现样本聚类.在多个合成数据集和真实数据集上的实验表明文中算法获得较优的聚类效果.

Adaptive Rulkov Neuron Clustering Algorithm

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.