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Concept based clustering for descriptive document classification

机译:基于概念的聚类,用于描述性文档分类

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References(15) We present an approach for improving the relevance of search results by clustering the search results obtained for a query string with the help of a Concept Clustering Algorithm. The Concept Clustering Algorithm combines common phrase discovery and latent semantic indexing techniques to separate search results into meaningful groups. It looks for meaningful phrases to use as cluster labels and then assigns documents to the labels to form groups. The labels assigned to each document cluster provide meaningful information on the various documents available under that cluster. This provides a more interactive and easier way to probe through search results and identifying the relevant documents for the users using the search engine.
机译:参考文献(15)我们提出了一种通过概念聚类算法对搜索字符串的搜索结果进行聚类的方法,以提高搜索结果的相关性。概念聚类算法结合了常用短语发现和潜在语义索引技术,可将搜索结果分成有意义的组。它寻找有意义的短语用作群集标签,然后将文档分配给标签以形成组。分配给每个文档集群的标签提供有关该集群下可用的各种文档的有意义的信息。这提供了一种更具交互性和更轻松的方式,可以搜索搜索结果并使用搜索引擎为用户标识相关文档。

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