Deep Web complex matching method based on association mining and semantic clustering
基于关联挖掘和语义聚类的Deep Web复杂匹配方法*

In order to improve the efficiency and accuracy of Deep Web interface matching, this paper presented a method based on the existing dual correlation mining (DCM) method using association mining and semantic clustering. While digging group attributed by using correlation algorithm, introduced and realized a new correlation measure based on mutual information by matrix to resolve the inefficiency problem. Clustered the attributes to synonymous attributes by their similarity which was computed by using semantic net. By the comparison on more than 200 interfaces in 4 domains, the experiment results indicate that the improved method has greatly heighted than DCM in the respect of efficiency and accuracy.