A Kernel Fuzzy Clustering Algorithm with Generalized Entropy Based on Weighted Sample

Aiming at fuzzy clustering with generalized entropy, a kernel fuzzy clustering algorithm with generalized entropy based on weighted sample is presented. By introducing weight of sample into objective function for fuzzy clustering with generalized entropy, we obtain optimization problem for fuzzy clustering with generalized entropy based on weighted sample. And we use Lagrange multiplier method to solve corresponding optimization problem and obtain degree of membership for each sample belonging to different cluster, centers of clusters and weights of samples. Following that, a kernel fuzzy clustering algorithm with generalized entropy based on weighted sample is presented. We select the representative dataset Iris from UCI repository to conduct experiments. Experimental results show the effectiveness of presented algorithm above.