The robust K-plane clustering algorithm can reduce the sensitivity of the traditional K-plane clustering algorithm to noises and the predefined number of clustering is not necessary.
本算法改进传统算法对噪声点敏感的缺点，并解决了传统超平面聚类初始需要指定聚类数目的不足。
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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