Synthesization of High-Utility Patterns in Parallel Computing

High utility pattern mining (HUPM) has become a key issue in knowledge discovery since it provides retailers and managers with useful information for making decisions efficiently. However, previous studies most focused on mining the high-utility patterns (HUPs) from a single database. In this paper, we present a framework to incorporate the weighted model for parallel synthesis of the discovered HUPs from various databases. The pre-large concept was also used as a buffer here in order to provide more prospective HUPs, thus providing higher accuracy of the synthesized patterns. From our experiments, the developed model exceeds existing works, in particular the designed model has increased precision and recall on knowledge synthesization compared to the previous works.