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This paper proposes a novel method to generate labels for grouping and organizing the search results returned by auxiliary search engines. It has applied statistical techniques to measure the quantities of co-occurrence keywords for forming the label matrix of them, and then agglomerated them into higher-level
The content of a text is mainly defined by keywords and named entities occurring in it. In particular for news articles, named entities are usually important to define their semantics. However, named entities have ontological features, namely, their aliases, types, and identifiers, which are hidden from their textual
of terms or keywords. The keywords for one cluster may not occur in the documents of other clusters. This is a data sparsity problem faced in clustering high-dimensional data. In the new algorithm, we extend the k-means clustering process to calculate a weight for each dimension in each cluster and use the weight values
-measure and the label quality was determined by similarity with the keywords that most frequently appear in the clusters. Based on the experimental results, preprocessing techniques can improve the cluster quality. LDA algorithm produces documents cluster by topic with cluster quality better than K-Means and Lingo. Word
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