基于關(guān)聯(lián)規(guī)則與聚類算法的查詢擴(kuò)展算法:針對(duì)信息檢索中查詢關(guān)鍵詞與文檔用詞不匹配的問題，提出一種基于關(guān)聯(lián)規(guī)則與聚類算法的查詢擴(kuò)展算法。該算法在第1 階段對(duì)
初始查詢結(jié)果的前N 篇文檔進(jìn)行關(guān)聯(lián)規(guī)則挖掘，提取含有初始查詢項(xiàng)的關(guān)聯(lián)規(guī)則構(gòu)建規(guī)則庫，并從中選取與查詢用詞關(guān)聯(lián)度最大的K 個(gè)詞
作為擴(kuò)展詞，與初始查詢組成新查詢后再次查詢，在第2 階段將新查詢結(jié)果進(jìn)行聚類分析并計(jì)算結(jié)果中每篇文檔的最終相關(guān)度，按最終相
關(guān)度大小重新排序。實(shí)驗(yàn)結(jié)果表明，該算法比單獨(dú)使用關(guān)聯(lián)規(guī)則算法或是單獨(dú)使用聚類算法均有更優(yōu)的檢索性能。
關(guān)鍵詞：信息檢索；查詢擴(kuò)展；關(guān)聯(lián)規(guī)則；聚類算法

【Abstract】To solve the problem of word-mismatch between query key words and document words, this paper puts forward a query expansion
algorithm based on the combination of association rules and cluster algorithm. At the first stage it uses association rules on the front N documents in
the first query result, and gets the rules that have query item to build the rules base, and gets the K words that have the most similarity with the query
words to form a new query and query again to get a new result. At the second stage it uses cluster algorithm on the new result and compute every
document’s final similarity to get a document re-ranking. Experimental result shows this query expansion algorithm outperforms both the association
rules and the cluster algorithm.
【Key words】information retrieval; query expansion; association rules; cluster algorithm