An immune-inspired evolutionary fuzzy clustering algorithm based on constrained optimization

It is possible to view the clustering problem as an optimization problem that locates the optimal centroids of the clusters directly under the membership function constraints, rather than finding an optimal partition. In this paper a new evolutionary approach to fuzzy clustering is introduced, which based on the application of artificial immune principles to the fuzzy c-means clustering algorithm. The theoretical aspects as well as experimental results are presented. The convergence and parameter setting are also discussed.