Therefore, the improved FCM clustering results can reduce the sampling errors and retain the main attributes of cloud classification samples.
改进后的聚类结果既消除了采样误差，又保持了云类样本的基本特征属性。
This paper proposes a text classification method based on Cloud Theory and neural network structure decision tree.
提出一种基于云理论和神经网络构造决策树的文本分类方法。
The classification based on cloud model always USES cloud transform and Pan-Concept-Tree, it brings on inconsistency between the result and prior knowledge.
基于云模型的分类算法多采用云变换和泛概念树方法，存在分类结果与先验知识不一致的问题。
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