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说明：  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。(In the original fcm algorithm based on the number of clusters on the algorithm and the weighted index m given c preferred method, and then out of the fcm parameter optimization adaptive algorithm, artificial data and data validation with real background can be seen that algorithm is effective, the adaptive algorithm can give the best clustering number, and can verify the validity of clustering, to achieve the best clustering purposes.)

FCM算法中参数的优选方法及实例应用.pdf
fcmC.m