使用元素和文档配置文件进行信息聚类

J. Lai, B. Soh
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引用次数: 5

摘要

近年来,可获得的信息量和访问Web站点的人数的巨大增长对信息过滤和检索提出了一些关键的挑战。网络访问者不仅希望获得高质量和相关的信息,而且希望信息以尽可能高效的方式呈现。传统的过滤方法只考虑文档的相关值。这些传统的方法没有考虑到文档检索的效率。在本文中,我们提出了一种基于文档元素计算文档相似度分数的新算法。使用索引,将派生文档概要文件。任何相似度得分高于给定阈值的文档都被聚类。使用这些预聚类文档,可以提高信息过滤和检索的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using element and document profile for information clustering
The tremendous growth in the amount of information available and the number of visitors to Web sites in the recent years poses some key challenges for information filtering and retrieval. Web visitors not only expect high quality and relevant information, but also wish that the information be presented in an as efficient way as possible. The traditional filtering methods, however, only consider the relevant values of document. These conventional methods fail to consider the efficiency of documents retrieval. In this paper, we propose a new algorithm to calculate an index called document similarity score based on elements of the document. Using the index, document profile will be derived. Any documents with the similarity score above a given threshold are clustered. Using these pre-clustered documents, information filtering and retrieval can be made more efficient.
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