The fuzzy c-means algorithm (FCM) is one of widely used clustering algorithms.
模糊c均值算法(FCM)是经常使用的聚类算法之一。
Considering fuzzy C-means clustering algorithms are sensitive to initialization and easy fall - en to local minimum, a novel optimization method is proposed.
针对模糊C均值聚类算法对初始值敏感、易陷入局部最优的缺陷，提出一种新的优化方法。
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。
Several new algorithms of fuzzy C-mean clustering with the combination of vector quantization are proposed for speaker identification.
该文提出了一种将模糊C -均值聚类法的各种改进算法与矢量量化法相结合的说话人辨认的新方法。
At last we deeply studies the methods of optimizing the structure of fuzzy clustering, and proposes two algorithms.
三是深入研究模糊聚类神经网络的结构优化方法，提出两种模糊聚类神经网络优化方案。
At last we deeply studies the methods of optimizing the structure of fuzzy clustering, and proposes two algorithms.
三是深入研究模糊聚类神经网络的结构优化方法，提出两种模糊聚类神经网络优化方案。
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