Document Retrieval by Hierarchical Multi Dimensional Scaling

本研究提出的方法是：1.构建包含词与词之间关系信息的知识库; 2.根据用户的观点从知识库中提取距离信息; 3.基于距离信息进行文档聚类。1知识库是通过使用词频的对应分析构建的。2文档相似性的评估是根据该方法利用“视图向量”来构造文档的投影空间，用户输入的关键词估计视图向量，在实验中得到了较好的文档聚类结果。然而，我们不能构造好的文档集群时，额外的关键字指定。当指定额外的关键字时，重新构建知识库给出了更好的结果。

This research proposed the method ;1 constructing knowledge base which contain the information about relationship between words,2 retrieving distance information from the above knowledge base according to user view,3 document clustering based on the above distance information.Features of this method is ;1 Knowledge base is constructed by correspondence analysis that uses the frequency of words.2 Assessment of documents similarity is calculated on projection space by "view vector".The keywords user inputted estimates view vector.This method constructs good document clusters in some experiments. However, we can not constructs good document clusters when additional keywords are specified. When additional keywords are specified, re-build knowledge base gives preferable results.