文档的内容分析和连接分析是计算文档相似度的两种方法。
Content analysis and link analysis among documents are two common methods in recommending system.
然后采用模糊c均值根据上述计算文档相似度的结果对文档进行聚类。
Then use Fuzzy C-means to do document clustering based on the results of similarity calculation above.
该文提出了一个新的基于综合语义的可扩展标记语言文档相似度计算方法。
This paper proposes a new method for XML document similarity computation based on the synthetical features of XML documents.
基于属性的重心剖分模型是一种较为新颖的文档相似度计算模型，但容易导致语义信息丢失和效率低下。
Documents similarity computing with attribute barycenter coordinate model is a relatively new method, but the semantic information easily loss and is inefficient.
在网络信息检索中，基于文档向量空间的分类、聚类、排序与相关性反馈需要计算相似度。
In network information retrieval, based on document vector space, class, cluster, ranking and relevance feedback need to compute similarity.
在信息匹配算法方面，通过计算文档向量之间的相似度，实现网络信息的有效过滤。
Finally, an algorithm for computing document similarity is presented, which filters abnormal information more efficiently.
如果你回顾余弦相似度的教科书的定义，你会发现它是在一个查询和文档的相应术语权重的产品的总和，归一化。
If you review the textbook definition of cosine similarity, you'll find that it's the sum of products of corresponding term weights in a query and a document, normalized.
本文就基于向量的相似度计算方案进行探讨，并论述了相似度在文档分类、聚类、排序与相关性反馈中的应用。
This paper explores vector-based similarity computing scheme, and discusses the application of similarity in class, cluster, ranking and relevance feedback.
TCU SS算法利用两个概念列表中单词间的语义相似度作为文档间相近程度的度量，并以图为基础进行聚类分析，避免有些聚类算法对聚簇形状的限制。
TCUSS algorithm measures the document similarity by semantic similarity of concepts in concept lists, then clusters the document based on graph analysis, thus avoiding the restrict of clusters shape.
相似度的计算在信息检索及文档复制检测等领域具有广泛的应用前景。
Text similarity counting has been widely used in several fields, for example, the field of copy detection and information retrieval, etc.
该方法监测系统资源使用情况，通过调整新闻时间窗口、文档向量维度和用户模型关键词维度的方式来动态调整相似度计算的运算量。
It monitors the free resource of the system and adjusts a sliding time window on news stream accordingly. This way, the dimensions of document vector and user profile vector change dynamically.
针对校园论坛中的文档数据进行聚类，该方法降低了处理的复杂度同时提高了相似度计算的准确性。
Do the experiment on the documents data of campus forum, this method reduces the computer processing complexity and improves the veracity of similarity calculation.
对用户的检索，进行查询词到主题词的转化，计算语义相似度，按照 语义相似度算法进行排序文档。
Second, we make a conversion from users' query to the thesaurus, then calculate the semantic similarity in the thesaurus network.
句子间相似度的计算在自然语言处理的各个领域都占有很重要的地位，在多文档自动文摘技术中，句子间相似度的计算是一个关键的问题。
Sentence similarity computation is very important in all the fields of Natural Language Processing. In Multi-document Summarization Technology, sentence similarity computation is a key problem.
句子间相似度的计算在自然语言处理的各个领域都占有很重要的地位，在多文档自动文摘技术中，句子间相似度的计算是一个关键的问题。
Sentence similarity computation is very important in all the fields of Natural Language Processing. In Multi-document Summarization Technology, sentence similarity computation is a key problem.
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