Since the semantic sequence is only related to text, it is available for incremental clustering.
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。
Experimental results show that the method can adaptively and efficiently achieve incremental clustering.
实验结果表明，该方法能动态有效地实现增量式聚类。
This paper firstly introduces the classification of incremental clustering algorithms and the research state, defines the concept of algorithm equivalence.
本文首先介绍了增量聚类算法的分类以及研究现状，提出了增量聚类算法等价性概念；
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.
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。
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.
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。
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