Clustering web search results using semantic information

Clustering web search results will help users finding relevant information quickly. Suffix tree clustering (STC) algorithm is well fit for clustering web documents. This paper puts forward an improved web search results clustering algorithm based on STC. It uses latent semantic indexing method to assist finding common descriptive and meaningful topic phrases for the final document clusters. Using semantic information for clustering web snippets is able to make search engine results easy to browse and help users quickly find web information interested. Evaluation of experiment results demonstrates that clustering web search results based on the improved suffix tree algorithm gets better performance in cluster label quality and snippets assignment precision.