Improved Search Results Clustering Algorithm Based on Suffix Tree Model
Deshan Liu · 2011
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 results 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 carrot2 framework was established and CQIG algorithm's classification accuracy and clustering label's discriminative and readability were confirmed on this platform.