Efficient algorithms for mining fuzzy rules in large relational databases

Mining association rules and sequential rules from large databases is an important task of data mining. Previous work is focused on definite and accurate concepts, which may not be concise and meaningful enough for human experts to easily obtain nontrivial knowledge from the rules discovered. The definition of fuzzy concepts is based on fuzzy set theory, which is especially useful when the discovered rules are presented to human experts for examination. Algorithms are presented for discovering fuzzy associating rules and fuzzy sequential rules expressed by fuzzy concepts from large relational databases.