个人数据空间中的文档聚类

Debao Liu, Dan Yang, Tiezheng Nie, Yue Kou, Derong Shen
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引用次数: 1

摘要

在个人数据空间(PDS)中,包含大量有用信息的文档在我们的日常工作中起着重要作用。然而,如何有效地管理这些文档中的信息是一个难题。在本文中,我们首先从文档中提取一些频繁出现的术语,然后基于这些术语对这些文档进行聚类。用户可以方便地根据文档的内容进行查询。实验证明了该方法中关键技术的准确性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Document Clustering in Personal Dataspace
In Personal Dataspace (PDS), documents containing a lot of useful information play an important role in our daily work. However, it is difficult to manage the information in these documents efficiently. In this paper, we first extract some frequent terms from documents, and then cluster these documents based on the terms. Thus users can query the documents based on their contents conveniently. The experiments demonstrate the accuracy and efficiency of the key techniques in our approach.
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