Discovery of direct and indirect fuzzy sequential patterns with multiple minimum supports in transaction databases

Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which have three limitations. Firstly, it can not concern quantitative attributes; secondly, only direct sequential patterns are discovered; thirdly, it can not process these data items with similar frequencies which will result in the dilemma called the rare item problem. In this paper, we put forward a discovery algorithm for mining both direct and indirect fuzzy sequential patterns with multiple minimum supports by combining these three extensions.