完全加权关联规则挖掘及其在查询扩展中的应用*

为了将完全加权关联规则挖掘技术应用于查询扩展，提出面向查询扩展的基于多种剪枝策略的完全加权词间关联规则挖掘算法,该算法能够极大地提高挖掘效率；提出了一种新的查询扩展模型和扩展词权重计算方法，使扩展词权值更加合理，在此基础上提出一种新的基于局部反馈的查询扩展算法,该算法利用完全加权关联规则挖掘算法自动从局部反馈的前列初检文档中挖掘与原查询相关的完全加权关联规则，构建规则库，从中提取与原查询相关的扩展词，实现查询扩展。实验结果表明，查询扩展算法的检索性能确实得到了很好的改善和提高，与现有查询扩展算法比较，在相同的查全率水平级下其平均查准率有了明显的提高。

Item-all-weighted association rules mining and its applications in query expansion

In order to combine the association rules mining technique with the query expansion, a new algorithm of item-all-weighted association rules mining for query expansion was presented based on multiplicate pruning. This method could tremendously enhance the mining efficiency. And a novel query expansion algorithm of local feedback was proposed based on item-all-weighted association rules mining. The algorithm could automatically mine those all-weighted association rules related to original query in the top-ranked retrieved documents, to construct an association rules-based database, and extract expansion terms related to original query from the database for query expansion. At the same time, a new computing method for weights of expansion terms was given. It makes the weighted value of an expansion term more reasonable. Experimental results show that our method is better than traditional ones in average precision.