摘要
数据库语义上的异构成为数据整合的重点和难点。针对该问题文章提出一种基于鱼群算法的异构数据库语义聚类算法。其思路是,首先对数据库属性信息进行向量化、矢量化,利用鱼群算法得到一个较优的聚类结果,再根据鱼群算法的结果使用模糊C-均值算法进行调整聚类。实例分析结果证明,该算法具有较高的聚类准确度。
The heterogeneity of database on semantics has become the priorities and difficulties. In the light of heterogeneity, a clustering algorithm based on fish swarm algorithm is proposed. The idea of the algorithm is that, first put the attribute information of database to quantification, vectorization, and use the fish swarm algorithm to obtain a better clustering results. Then on this basis the FCM is applied to adjust the results. The result of the experiment shows that it has a high clustering accuracy.
出处
《计算机与数字工程》
2013年第1期12-13,20,共3页
Computer & Digital Engineering
关键词
鱼群算法
异构数据库
语义聚类
语义异构
fish swarm algorithm, heterogeneous database, semantic clustering, semantic heterogeneity
