A Convergence Theorem for Improved Kernel Based Fuzzy C-Means Clustering Algorithm

In 2008, we proposed a clustering algorithm called improved kernel based fuzzy c-means clustering algorithm (IKFCM) to improve the performance of the original fuzzy c-means clustering algorithm. In this paper, we analyze the convergence of the IKFCM by means of Zangwill´s convergence theorem. The result shows that arbitrary sequences generated by IKFCM always terminates at a local minimum or saddle point, or at worst, al-ways contains a subsequence which converges to a local minimum or saddle point of the IKFCM clustering model.