Combined with semantic similarity of text data, this paper gives a method of text data clustering based on semantic density.
结合文本数据的语义相似度，给出一种基于语义密度文本数据聚类的方法。
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。
This paper presents a new method of text clustering by using the latent semantic index (LSI) and self-organizing neural network (SNN).
根据隐含语义索引（LSI）理论和动态自组织映射神经网络理论，提出了一种文本聚类的新方法。
This paper proposes a new method for meaning clustering called 'Supervised Probabilistic Latent Semantic Indexing' (SPLSI).
本文提出了一种语义聚类和扩展的新方法，称为有指导的统计隐含语义标引(SPLSI)算法。
This paper tries to make some improvements on applying Self-Organizing-Map (SOM) to automatic clustering of Chinese nouns, so as to generate a better Chinese semantic map.
本文试图对自组织映射神经网络（SOM）应用于汉语名词语义自动聚类做某些改进。
Then, the relationship between clustering regions and semantic concepts is established according to the labeled images in the training set.
然后，根据训练集中样本图像的标注情况建立图像区域与语义关键字的关联。
Then, the relationship between clustering regions and semantic concepts is established according to the labeled images in the training set.
然后，根据训练集中样本图像的标注情况建立图像区域与语义关键字的关联。
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