Optimal clustering using neural networks
Jung-Hua Wang, C. H. Chung · 2002
This paper proposes an improved clustering method based on a neural network. The improved method does need not pre-specify the number of clusters. In order to obtain more accurate and computation-efficient clustering results, the proposed method adaptively computes the optimal threshold for each cluster separately, instead of calculating minimum spanning tree for determining a fixed global threshold. Simulation results show that in terms of accuracy and computation time, the proposed method provides superior performance to that of the traditional k-means method.