{'fraction_non_alphanumeric': 0.021248339973439574, 'fraction_numerical': 0.0, 'mean_word_length': 4.937007874015748, '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}}
Efficient Mining of Maximal Frequent Itemsets Based on M-Step Lookahead

We propose a new maximal frequent itemset mining algorithm, named m-step lookahead. This is a variant of the Max-Miner algorithm that, instead of counting the support of the largest possible superset of each candidate itemset, counts the support of a superset with a predetermined length. This is designed to circumvent the weakness in the Max-Miner algorithm that the probability of finding a frequent superset is extremely low for the first several passes. By looking for a smaller superset, m-step lookahead may find long frequent patterns more quickly than Max-Miner. Our experimental results demonstrate that this is the case for certain datasets and user-defined parameters.