An Improved Density-Based Clustering Algorithm Based on Cellular Neural Networks

Clustering plays a major role in various disciplines including biological visual image classification. Various clustering algorithms have been introduced and developed. Among them, the density-based clustering algorithm achieves the best performance. The cellular neural network (CNN) based clustering algorithm (CNNCA) is a density-based method. It performs the clustering at a very high speed because the CNN can be implemented on a silicon chip. Nevertheless, the performance of the CNNCA is significantly degraded when facing clusters of complex structures, especially with significant density differences between clusters. In this paper, the local rules of the CNNs are modified. Moreover, the multi-spatial resolutions are employed. In particular, the values of minimum spatial resolution and the other parameters are adaptively designed. Hence, the iterative procedures can be applied to the remaining unassigned points. Furthermore, the conditional minimum distance rule is employed to assign the dispersed elements to the existing clusters. Different datasets have been utilized to evaluate the performance of our proposed method. Compared to the state-of-the-art clustering algorithms, the computer numerical simulation results show that our proposed method outperforms the existing state-of-the-art methods in higher accuracy and more robustness.