Mining Interesting XML-Enabled Association Rules with Templates

XML-enabled association rule framework [FDWC03] extends the notion of associated items to XML fragments to present associations among trees rather than simple-structured items of atomic values. They are more flexible and powerful in representing both simple and complex structured association relationships inherent in XML data. Compared with traditional association mining in the well-structured world, mining from XML data, however, is confronted with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. In order to make XML-enabled association rule mining truly practical and computationally tractable, in this study, we present a template model to help users specify the interesting XML-enabled associations to be mined. Techniques for template-guided mining of association rules from large XML data are also described in the paper. We demonstrate the effectiveness of these techniques through a set of experiments on both synthetic and real-life data.