{'fraction_non_alphanumeric': 0.01728395061728395, 'fraction_numerical': 0.0032921810699588477, 'mean_word_length': 5.300518134715026, '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}}
Towards visualization of rules based on rough sets and graphical modeling

Rule induction methods have been introduced since 1980's and many applications show that they are very useful to acquire simple patterns from large databases. However, when a database is very large, the methods generate too many rules, which makes domain experts interpret all the rules. Moreover, since rules only shows the relations between attribute-value pairs, it is very difficult to capture the relations between concepts or among induced rules. In order to solve this problem, many kinds of visualization has been introduced. Rough set theory has a technique on conflict analysis with qualitative distance obtained from attributes, which gives graphical relations between class or rules. On the other hand, statistical methods have a graphical model method, which gives graphical relations between attributes by using partial coefficients or other indices. In this paper, we introduce a new approach which combines conflict analysis and graphical modeling. The results show that the combination of these two methods gives the other type of visualization of rules, which gives also a formal mathematical model for rule visualization.