The iterative algorithms in C language were given, which used direct data replacement to reduce the dimension of matrix and saved the storage space.
在迭代算法中，采用直接数据置换降低矩阵维数，节省了存储空间，提高了运算速度。
In this article we combine the fuzzy C-means algorithm with fuzzy measures and fuzzy integrals and apply the two algorithms to the medicinal pathological image segmentation.
本文将经典的模糊c -均值聚类算法和模糊测度和模糊积分结合起来，并将这两种算法应用于医学病理图象的分割。
Fuzzy C-means clustering is one of the important learning algorithms in the field of pattern recognition, which has been applied early to image segmentation.
模糊c -均值聚类是模式识别中的重要算法之一，很早就被应用到图像分割中。
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