{'fraction_non_alphanumeric': 0.012315270935960592, 'fraction_numerical': 0.0, 'mean_word_length': 5.253846153846154, '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}}
Mining Fuzzy Frequent Item Sets

Due to various reasons transaction data often lack information about some items. This leads to the problem that some potentially interesting frequent item sets cannot be discovered, since by exact matching the number of supporting transactions may be smaller than the user-specified minimum. In this study we try to find such frequent item sets nevertheless by inserting missing items into transactions during the mining process in order to allow approximate matching. We present a recursive elimination algorithm, based on a step by step elimination of items from the transaction database together with a recursive processing of transaction subsets. This algorithm is very simple, works without complicated data structures, and allows us to find fuzzy frequent item sets easily.