Optimal Number of Clusters and the Best Partition in Fuzzy C-mean
模糊C-均值中的最优聚类与最佳聚类数

We construct a new and simple classify rule based on intra-distance and inter-distance of fuzzy C-means (FCM) and nests ISODATA and GA to construct the GA-ISODATA in order to perform optimization computing of the FCM. Comparing with similar methods, this method can not only complete the optimal partition on the promise of giving the number of pre-classify, but also directly get the optimal number of classify in FCM without people-engaged. We only need to change the fitness function in GA when doing optimization computing for different classify rules, so GA-ISODATA also fits for optimal classify and the computing of optimal classify number of other rules.