{'fraction_non_alphanumeric': 0.029473684210526315, 'fraction_numerical': 0.035789473684210524, 'mean_word_length': 5.52054794520548, '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}}
Self-organizing maps of massive document collections

Huge document collections can be organized according to textual similarities by the self-organizing map (SOM) algorithm, when statistical representations of the textual contents are used as the feature vectors of the documents. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240-node SOM. For the feature vectors we selected 500-dimensional random projections of the weighted word histograms.