Query expansion of local feedback based on frequent itemsets and negative rules
Zhong Zhi · Jisuanji gongcheng yu sheji · 2012
Aiming at the term mismatch issues of existing information retrieval system,a novel query expansion model and its algorithm of local feedback is proposed based on frequent itemsets and negative association rules mining.Firstly,the frequent itemsets and non-frequent itemsets are mined synchronously in the top-ranked n chapter retrieved local documents.On one hand,the association terms are extracted from the frequent itemsets,on the other hand,negative association rules are mined in frequent itemsets and non-frequent itemsets and the consequents of negative association rules are extracted to make into negative association term.And then,final negative association terms are obtained according to the correlation of each negative association term and the entire original query.Finally,the terms the same as negative association terms are removed from association terms database and the rest of the terms of the association terms database are combined with original query for query expansion.The experimental results show that the proposed algorithm can not only detect those false negative association terms but also effectively improve and enhance the information retrieval performance.