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。
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