Mining Time-Interval Sequential Patterns with High Utility from Transaction Databases

The purpose of time-interval sequential pattern mining is to help superstore business managers promote product sales. Sequential pattern mining discovers the time interval patterns for items: for example, if most customers purchase product item A, and then buy items B and C after r to s and t to u days respectively, the time interval between r to s and t to u days can be provided to business managers to facilitate informed marketing decisions. We treat these time intervals as patterns to be mined, to predict the purchasing time intervals between A and B, as well as B and C. Nevertheless, little work considers the significance of product items while mining these time-interval sequential patterns. This work extends previous work and retains high-utility time interval patterns during pattern mining. This type of mining is meant to more closely reflect actual business practice. Experimental results show the differences between three mining approaches when jointly considering item utility and time intervals for purchased items. In addition to yielding more accurate patterns than the other two methods, the proposed UTMining_Lambda method shortens execution times by delaying join processing and removing unnecessary records.