Incremental mining of high utility sequential patterns using MapReduce paradigm

High utility sequential pattern (HUSP) mining considers the nonbinary frequency values of items purchased in a transaction and the utility of each item. Incremental updates are very common in many real-world applications. Mining the high utility sequences by rerunning the algorithm every time when the data grows is not a simple task. Moreover, the centralized algorithms for mining HUSPs incrementally cannot handle big data. Hence, an incremental algorithm for high utility sequential pattern mining using MapReduce para-digm (MR-INCHUSP) has been introduced in this paper. The proposed algorithm includes the backward mining strategy that profoundly handles the knowledge acquired from the past mining results. Further, elicited from the co-occurrence relation between the items, novel sequence extension rules have been introduced to increase the speed of the mining process. The experimental results exhibit the performance of MR-INCHUSP on several real and synthetic datasets.