前言：目的探讨颅脑mri图像模糊聚类分割算法中最佳模糊聚类数。
Objective: To discuss the best fuzzy clustering number of MRI brain images segmentation.
结果当模糊聚类数为5 ~6时，模糊聚类有效性函数最小，图像处理的效果达到最佳水平。
Results: When fuzzy clustering number for 5-6 and fuzzy clustering validity achieved a minimum level of image processing with the best effect.
对大词汇量汉语连续语音识别的实验结果表明：高斯模糊聚类使高斯数减少25%时，识别率提高了0.15%。
The experimental results on large vocabulary continuous Mandarin speech recognition show when the number of Gaussians is reduced by 25%, the recognition accuracy increases by 0.15%.
该模型利用模糊聚类技术确定系统的模糊空间和模糊规则数，利用BP算法调整模糊神经网络的权系数。
The fuzzy space and the number of fuzzy rules of this model are defined by the fuzzy clustering method and weight coefficients of the model are adjusted by the BP algorithm.
探讨了聚类分析这一重要的数据挖掘方法在综合评价中的应用，将模糊聚类与综合评价相结合以解决待评价方案数较多的排序问题，并且文中还改进了建立模糊相似矩阵的方法。
Fuzzy clustering is associated with comprehensive assessment in the study of sorting when the number of object is large, and we improve the method of setting up fuzzy similar matrix.
利用两个聚类效果评价指标模糊效果指数FPI和归一化分类墒NCE，确定了最适宜的分区数。
Performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimum cluster number.
针对一类特征指标值及指标权重均为三角模糊数的多指标信息聚类问题，提出了一种新的最大树聚类分析方法。
With respect to multiple attribute clustering analysis problems with triangular fuzzy numbers, a new clustering analysis method is proposed.
针对一类特征指标值及指标权重均为三角模糊数的多指标信息聚类问题，提出了一种新的最大树聚类分析方法。
With respect to multiple attribute clustering analysis problems with triangular fuzzy numbers, a new clustering analysis method is proposed.
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