Processing heterogeneous XML data from multi-source

Recently XML heterogeneity has become a new challenge. In this paper, a novel clustering strategy is proposed to regroup these heterogeneous XML sources, for searching in a relatively smaller space with certain similarity can reduce cost. The strategy consists of four steps. We at first extract features about paths and map them into High-dimension Vector Space (HDVS). In the data pre-process, two algorithms are applied to diminish the redundancies in XML sources. Then heterogeneous documents are clustered. Finally, Multivalued Dependency (MVD) is introduced, for MVD can be redefined according to the range of constraints of XML. This paper also proposes a novel algorithm that discovering minimal MVD, based on the rough set handling non-integrity data. It can solve the problem that non-integrity data of XML influence on finding the MVD of XML, thus patterns can be extracted from each cluster.