Mining Positive and Negative Fuzzy Multiple Level Sequential Patterns in Large Transaction Databases

Sequential patterns mining is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which has three limitations. Firstly, it can not concern quantitative attributes; secondly, only positive sequential patterns are discovered; thirdly, it can not process these data items with multiple level concepts. Mining fuzzy sequential patterns has been proposed to address the first limitation. In this paper, we put forward a discovery algorithm for mining negative multiple level sequential patterns to resolve the second and the third limitations, and a discovery algorithm for mining both positive and negative fuzzy multiple level sequential patterns by combining these three extensions.