{'fraction_non_alphanumeric': 0.015172413793103448, 'fraction_numerical': 0.0, 'mean_word_length': 5.722222222222222, '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}}
A novel mining Method of Global Negative Association Rules in Multi-Database

Mining Negative Association Rules in multi-database has attracted more and more attention. Most existing research focuses on unifying all negative rules discovered from different single databases into a single view. This paper presents a novel method for mining global negative association rules in multi-database. This method produces some infrequent itemsets of potential interest by scanning constructed Multi-Database Frequent Pattern tree, and extracts negative association rules of interest according to the proposed correlation model from multi-database. Experimental results show the effectiveness and efficiency of the proposed algorithm.