Attribute means clustering is more robust than fuzzy means clustering by theoretical analysis and numerical example.
通过理论分析，属性均值聚类是比模糊均值聚类更稳健的聚类方法。
Consequently, with robust IT2PCM clustering algorithm as main tool, a rapid-prototyping approach to interval type-2 fuzzy modeling is proposed.
以鲁棒it 2 P CM算法为主要工具，建立了一种快速原型方法进行区间类型2模糊建模。
Besides, it was robust to the noises because it improved the constraint conditions used in the existing intuitionistic fuzzy clustering algorithm.
同时改进了现有的直觉模糊聚类算法中的概率型约束条件，使其对噪声和野值点具有较好的鲁棒性。
Besides, it was robust to the noises because it improved the constraint conditions used in the existing intuitionistic fuzzy clustering algorithm.
同时改进了现有的直觉模糊聚类算法中的概率型约束条件，使其对噪声和野值点具有较好的鲁棒性。
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