To ameliorate the long-standing problems of theme drift and word mismatch in natural language processing applications

this paper first proposes a computing method for weighted itemset support and a pruning method based on item weight sorting (IWS). And then

a weighted association rule mining algorithm for query expansion is presented based on the IWS

and the models such as association rule antecedent and consequent hybrid expansion (RACHE)

rule consequent expansion (RCE) along with rule antecedent expansion (RAE) are discussed. Finally

an algorithm of cross-language query expansion (CLQE) is put forward based on the IWS mining. The algorithm utilized the new support and the pruning method to mine the weighted association rules

and extracted high quality expansion terms from the rules according to the expansion models in order to carry out CLQE. A comparison between the proposed expansion algorithm and the existing CLQE algorithms based on weighted association rules mining is made

which shows that the former can effectively restrain the problems of query topic drift and word mismatch

and can be used in information retrieval in various languages to improve retrieval performance. The RCE achieves the optimal retrieval performance in the proposed expansion models

and the retrieval performance of the RACHE is not as good as that of the RAE and the RCE. The support is more effective for the RCE algorithm. The confidence can make the RAE and the RACHE get the best retrieval result. And moreover

the proposed mining method can be used in text mining

business data mining and recommendation system to improve its mining performance.