基于关联规则的检索结果聚类优化

Search Results Clustering Optimization Based on Association Rules

根据元搜索引擎以线性列表的方式为用户提供检索结果的现象,提出一种基于关联规则的检索结果聚类优化方法,在经过分词处理后,提取检索结果中标题和摘要的主要关键词集,从而建立关联词矩阵(AWM)及基于TFIDF函数表示的结果特征向量,实现基于AWM的FCM聚类。仿真实验结果表明,该方法能够提高运行效率及聚类的有效性。

According to the fact that meta search engines present the search results to the end user with linear lists, a search results clustering optimization method based on association rules is proposed. The objective is to extract the main keyword sets after segmenting the subject and abstract of the search results to build up Associated Word Matrix（AWM） and express the result feature vector based on TFIDF function, so as to realize FCM clustering based on AWM. Simulation experimental results show this method can promote running efficiency and cluster effect.