Query Expansion of Local Feedback Based on Improved Apriori Algorithm
Chen Yanhong1 Huang Mingxuan2
1(College of Physical Science, Guangxi University, Nanning 530004, China) 2(Department of Mathematics and Computer Science,Guangxi College of Education,Nanning 530023, China )
An improved Apriori algorithm for query expansion is presented based on the thrice pruning strategy. This method can tremendously enhance the mining efficiency. After studying the limitations of existing query expansion，a novel query expansion algorithm of local feedback is proposed based on the improved Apriori algorithm.This algorithm can automatically mine those association rules related to original query in the top-rank retrieved documents using the improved Apriori algorithm, to construct an association rules-based database, and extract expansion terms related to original query from the database for query expansion. Experimental results show that our method is better than traditional ones in average precision.
陈燕红,黄名选. 基于Apriori改进算法的局部反馈查询扩展[J]. 现代图书情报技术, 2007, 2(9): 84-87.
Chen Yanhong,Huang Mingxuan. Query Expansion of Local Feedback Based on Improved Apriori Algorithm. New Technology of Library and Information Service, 2007, 2(9): 84-87.
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