Mining Cluster Patterns in XML Corpora via Latent Topic Models of Content and Structure

We present two innovative machine-learning approaches to topic model clustering for the XML domain. The first approach consists in exploiting consolidated clustering techniques, in order to partition the input XML documents by their meaning. This is captured through a new Bayesian probabilistic topic model, whose novelty is the incorporation of Dirichlet-multinomial distributions for both content and structure. In the second approach, a novel Bayesian probabilistic generative model of XML corpora seamlessly integrates the foresaid topic model with clustering. Both are conceived as interacting latent factors, that govern the wording of the input XML documents. Experiments over real-world benchmark XML corpora reveal the overcoming effectiveness of the devised approaches in comparison to several state-of-the-art competitors.