Fuzzy multiple-level sequential patterns discovery from customer transaction database

Sequential patterns discovery is a very important research topic in data mining and knowledge discovery, and it has been widely applied in business analysis. Previous works were focused on mining sequential patterns at a single concept level based on definite and accurate concept which may not be concise and meaningful enough for human experts to easily obtain nontrivial knowledge from the rules discovered. In this paper, we introduce concept hierarchies firstly, and then discuss a mining algorithm F-MLSPDA for discovering multiple-level sequential patterns with quantitative attribute based on fuzzy partitions.