In recent years there have been some significant research towards the ability of processing related data, particularly the relatedness among atomic elements in a structure with those in another structure. A number of approaches have been developed with various degrees of success. This chapter provides an overview of machine learning approaches for the encoding of related atomic elements in one structure with those in other structures. The chapter briefly reviews a number of unsupervised approaches for such data structures which can be used for solving generic classification, regression, and clustering problems. We will apply this approach to a particularly interesting and challenging problem: The prediction of both the number and their locations of the in-links and out-links of a set of XML documents. In this problem, we are given a set of XML pages, which may represent web pages on the Internet, with in-links and out-links. Based on this training dataset, we wish to predict the number and locations of in-links and out-links of a set of XML documents, which are as yet not linked to other existing XML documents. To the best of our knowledge, this is the only known data driven unsupervised machine learning approach for the prediction of in-links and out-links of XML documents.
Hagenbuchner, M., Tsoi, A. C., Kc, M. & Zhang, S. (2012). Data driven encoding of structures and link predictions in large Xml document collections. In A. Tagarelli (Eds.), XML data mining : models, methods, and applications (pp. 219-241). Hershey, PA: Information Science Reference.