A Study On The Technique Of Information Acquirement Based On Meta-Search And Content Clustering

Web has become the main resource to acquire information, and the Search Engine is main tool. However, the information acquired is still unsatisfactory. Users cannot distinguish the useful information from enormous unstructured search results. Users desire to get good information with high efficiency, conquer information overload and harness the true power of information.In this paper, to promote the efficiency and quality of information acquisition, we study and develop the system of information acquirement based on Meta-search and content clustering techniques with following approaches: (1) we propose a whole frame with three modules: search module, operation module and user module, their work flow are introduced too. (2) Promote the method of analyzing title and abstract of web page to judge relevance of search results. The experiment proved the improvement of average veracity comparing with the member search engines. (3) Put forward a clustering method based on meta-search and two clustering algorithms--K-means and BIRCH. The evaluation of experiment shows the improvement on clustering results and efficiency. (4) In system design and realization aspects, we introduce the database system, software system, and human-machine interface, it can complete the three functions, i.e. the information retrieval based on title and abstract analysis, clustering based on meta-search and two clustering algorithms--K-means and BIRCH, and multidimensional analysis based on OLAM.