Research Of Chinese Meta Search Engine Based On Clustering

With the rapid development of Internet and communication technology, the information resources on Internet increase sharply. Search engine emerges and develops fast as a major tool of Internet information retrieval. But every search engine has a specific database index, the unique features and expected user base. People often use several search engines for search results'comparison and screening. To make full and effective use of available information resources and reduce relative investment, people start to pay special attention to Meta Search Engine. But now most of them present the search results to the end user with linear list,as there are thousands of the search results,it make user spend much time to find what they really want. Accordingly, clustering the search results is one efficient solution to improve the user's searching experience.In this thesis, firstly the Chinese Meta Search Engine technique was studied. Then we discussed the Suffix Tree Clustering algorithm and proposed a Improved Suffix Tree Clustering algorithm for Chinese. We disigned and implemented a Chinese Meta Search Engine ISTCC which provided the function of search results clustering. Finally, we designed two experiments to validate the improved ranking algorithm of search results and the Improved Suffix Tree Clustering algorithm for Chinese.