Mining Association Rules from Structural Deltas of Historical XML Documents

Abstract

Previous work on XML association rule mining focuses on mining from the data existing in XML documents at a certain time point. However, due to the dynamic nature of online information, an XML document typically evolves over time. Knowledge obtained from mining the evolvement of an XML document would be useful in a wide range of applications, such as XML indexing, XML clustering. In this paper, we propose to mine a novel type of association rules from a sequence of changes to XML structure, which we call XML Structural Delta Association Rule (XSD-AR). We formulate the problem of XSD-AR mining by considering both the frequency and the degree of changes to XML structure. An algorithm, which is derived from the FP-growth, and its optimizing strategy are developed for the problem. Preliminary experiment results show that our algorithm is efficient and scalable at discovering a complete set of XSD-ARs.