Unsupervised Kernel Fuzzy Clustering Algorithm Based on Simulated Annealing
MA Si-liang · Journal of Jilin University(Science Edition) · 2009
As a generalization of the conventional possibilistic and kernel based possibilistic clustering model,a new kernel based possibilistic clustering model was proposed.The new approach performs the clustering by optimizing the proposed kernel possibilistic Xie-Beni index using the RJMCMC(Reversible Jump Markov Chain Monte Carlo) based simulated annealing algorithm(SA),which makes the number of clusters change in a given range and the optimal number of clusters and partitioning obtained automatically.In contrast to the conventional SA based possibilistic or kernel based possibilistic clustering,it has a higher efficiency and avoids the problem of generating coincident clusters.The contrast experiments on real and artificial data show the effectiveness of the proposed algorithm.