Abstract
This paper aims to detect high utility sequential patterns including time intervals and multiple utility thresholds. There are many algorithms that mine sequential patterns considering utility factor, these can find the order between the items purchased but they exclude the time interval among items. Further, they consider only the same utility threshold for each item present in the dataset, which is not convincing to assign equal importance for all the items. Time interval of items plays a vital role to forecast the most valuable real-world situations like retail sector, market basket data analysis etc. Recently, UIPrefixSpan algorithm has been introduced to mine the sequential patterns including utility and time intervals. Nevertheless, it considers only a single minimum utility threshold assuming the same unit profit for each item. Hence, to solve the aforementioned issues, in the current work, we proposed UIPrefixSpan-MMU algorithm by utilizing a pattern growth approach and four time constraints. The experiments done on real datasets prove that UIPrefixSpan-MMU is more efficient and linearly scalable for generating the time interval sequences with high utility.
