Effective data discretization can obviously improve system ability on clustering instances, and can also make systems more robust to data noise.
有效的离散化可以显著地提高系统对样本的聚类能力，增强系统对数据噪音的鲁棒性。
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。
Gaussian Mixture Density Modelling and Decomposition (GMDD) is a hierarchical clustering method based on robust statistical theory.
高斯混合密度降解模型(GMDD)是一种基于稳健统计理论的层次聚类方法。
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。
应用推荐