{'fraction_non_alphanumeric': 0.017162471395881007, 'fraction_numerical': 0.0, 'mean_word_length': 5.076388888888889, '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}}
Condensed representation of frequent itemsets

One of the major problems in pattern mining is still the problem of pattern explosion, i.e., the large amounts of patterns produced by the mining algorithms when analyzing a database with a predefined minimum support threshold. The approach we take to overcome this problem aims for automatically inferring variables from the patterns found, in order to generalize those patterns by representing them in a compact way. We introduce the novel concept of meta-patterns and present the RECAP algorithm. Meta-patterns can take several forms and the sets of patterns can be grouped considering different criteria. These decisions come as a trade-off between expressiveness and compaction of the patterns. The proposed solution accomplishes good results in the tested dataset, reducing to less than half the amount of patterns found.