A New Visual Theoretic Clustering Algorithm
Shitong Wang · 2004
The main contribution of this paper is to present a new visual theoretic clustering algorithm, which integrates visual systems together with the famous Weber law in biophysics to realize effective and nonparametric clustering. A new cost criterion for clustering is also presented. Our simulations demonstrate that this new nonparametric clustering algorithm is effective for nonlinearly separable datasets which in general the conventional clustering approaches such as FCM can not well cope with.