Research And The Implementation Of Query Recommendation Algorithm Based On Text Clustering Search Engine

The increasing speed of information shoots up after the birth of Internet. How to find the information people need? Search Engine provides us an efficient way to get the information we need. However the expanding speed of information is far beyond people’s imagination. The general search engine can no longer satisfy people’s demand. It’s a big challenge for search engine business that how to manage the amount of oceans of information with diversity, and how to present them to users in a reasonable and proper way. The technologies like Data Ming, Pattern Recognition, Semantic Web, Ontology, and Query Recommendation are commonly used to solve the problems of search engine.In the first, the history of search engine, its present situation home and abroad, the fundamental principle of general search engine, challenges of text retrieve based search engine were introduced in the beginning of the paper. Then query recommendation-the focus topic of this paper-was described in the current research fields. In the following the algorithm, named query recommendation algorithm based on text clustering search engine which approached in this paper, was demonstrated in detail from the aspects of clustering algorithms’selection strategy, recommend words’weight recalculation, similarity calculation methods, and so on. The algorithm provides a solution for the deep search of text clustering search engine. Some improvements were introduced into this algorithm particularly based on the specific text clustering search engine prototype application implemented in this paper. In the end introduces the design of modules and database for the text clustering application and verifies the algorithm’s feasibility and effectiveness.