Mining Positive and Negative Fuzzy Sequential Patterns in Large Transaction Databases

Abstract

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 two limitations. First, it can not concern quantitative attributes; second, only positive sequential patterns are discovered. Mining fuzzy sequential patterns has been proposed to address the first limitation. In this paper, we put forward a discovery algorithm for mining negative sequential patterns to resolve the second limitation, and a discovery algorithm for mining both positive and negative fuzzy sequential patterns by combining these two approaches.