{'fraction_non_alphanumeric': 0.026973026973026972, 'fraction_numerical': 0.0, 'mean_word_length': 5.147239263803681, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Classification de documents XML à partir d'une représentation linéaire des arbres de ces documents

In this work, we propose a new clustering document representation for semi-structured documents collections. Our approach consists on a representation of XML documents based on their sub-paths, defined according to some criteria (length, root beginning, leaf ending) using the structure only or both the structure and the content. By considering those sub-paths as words, we can use standard methods for vocabulary reduction, and simple clustering methods such as K-means that scale up well. We actually use an implementation of the clustering algorithm known as \textit{dynamic clouds} that can work with distinct groups of independent variables. This is necessary in our model since embedded sub-paths are not independent. For validation and evaluation of our method, two collections are used: the INEX corpus and the INRIA activity reports, and a set of metrics well-known in Information Retrieval.