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Mining XML Documents

Laurent Candillier 1 Ludovic Denoyer 2 Patrick Gallinari 2 Marie-Christine Rousset 3 Alexandre Termier 4 Anne-Marie Vercoustre 5, 6
1 GRAppA - LIFL - Groupe de Recherche en Apprentissage Automatique
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
3 LSR-IMAG - Laboratoire Logiciels Systèmes Réseaux
4 Institute of Statistical Mathematics
5 INRIA Rocquencourt
6 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
Abstract : XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the increasing size of XML collections as information sources, mining techniques that traditionally exist for text collections or databases need to be adapted and new methods to be invented to exploit the particular structure of XML documents. Basically XML documents can be seen as trees, which are well known to be complex structures. This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. We focus on three mining tasks: classification and clustering which are standard for text collections; discovering of frequent tree structure which is especially important for heterogeneous collection. This chapter presents some recent approaches and algorithms to support these tasks together with experimental evaluation on a variety of large XML collections.
Keywords : Bayesian networks stochastic generative model feature selection frequent trees XML Mining structured documents tree-based model document representation classification clustering frequent patterns
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Submitted on : Monday, November 19, 2007 - 3:55:34 PM
Last modification on : Wednesday, April 6, 2022 - 3:48:20 PM
Long-term archiving on: : Monday, April 12, 2010 - 2:42:34 AM

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CNRS | INRIA | UNIV-LILLE3 | MOSTRARE | UPMC | LIP6 | SORBONNE-UNIVERSITE | SU-SCIENCES | SU-TI | INRIA2

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Laurent Candillier, Ludovic Denoyer, Patrick Gallinari, Marie-Christine Rousset, Alexandre Termier, et al.. Mining XML Documents. P. Poncelet, F. Masseglia, M. Teisseire. Data Mining Patterns: New Methods and Applications, Information Science Reference, pp.198-219, 2007, ⟨10.4018/978-1-59904-162-9.ch009⟩. ⟨inria-00188899⟩

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