fcmC

  在原始的 fcm 算法基础上，对算法中的聚类数 c 和加权指数 m 给出优选方法， 进而而出了 fcm 参数优选自适应算法，通过人造数据与具有实际背景的数据验证可以看出 该算法是有效的，该算法不但可以自适应的给出最佳的聚类数，而且可以验证聚类的有效性， 达到最佳聚类的目的(Fcm algorithm in the original, based on the number of clustering algorithms and the weighted index m given c preferred method, and then out of the fcm parameter optimization adaptive algorithm, synthetic data and practical background in data validation can be seen that the algorithm is effective, the adaptive algorithm can not only give the best number of clusters, and clustering can verify the validity of the purpose to achieve the best clustering)