关键词: 搜索结果聚类，后缀树模型，向量空间模型，奇异值分解

Abstract: To make up for the deficiencies in clustering label selection, clustering quality evaluating and the control of overlapping clustering in the existing search results classification algorithm, this paper proposed an improved search resups clustering algorithm based on vector space model and suffix tree model. We modified LINGO algorithm's clustering function and clustering label scoring function,basic clustering merging process was added and the treatment effect of Chinese was improved. Finally, we analyzed the algorithm's classification results and the generated label's quality according to the experiment results. What' s more, a platform for recommended Web search results clustering based on carrotz framework was established and CQIG algorithm's classification accuracy and clustering label’s discriminative and readability were confirmed on this platform.

Key words: Search results clustering, Suffix tree model, Vector space model, Singular value decomposition