在传统层次聚类基础上，提出并实现了一种基于距离的增量式聚类算法，并应用于粮食智能决策支持系统中。
In this paper an incremental distance cluster arithmetic based on traditional level cluster arithmetic is proposed and realized.
最后，使用增量式的概念格生成算法对搜索结果片段进行概念聚类，并从中产生每个聚类的主题。
Finally, incremental algorithm of producing concept lattice is used to carry on concept clustering to the passage of search results, and produced the theme of each cluster result from it.
由于所提算法的语义序列只与文本自身相关，所以它适用于增量式聚类。
Since the semantic sequence is only related to text, it is available for incremental clustering.
根据简化算法的简化方式可将其分为四大类：顶点聚类算法、增量式简化算法、采样算法和自适应细分算法。
The model simplification algorithms can be divided into four main categories: vertex clustering, incremental simplification, the sampling algorithm and adaptive subdivision.
根据简化算法的简化方式可将其分为四大类：顶点聚类算法、增量式简化算法、采样算法和自适应细分算法。
The model simplification algorithms can be divided into four main categories: vertex clustering, incremental simplification, the sampling algorithm and adaptive subdivision.
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