Discovering Sequential Patterns by Neural Networks
Jakub Nowak, Marcin Korytkowski, Rafał Scherer · 2020
Sequential pattern mining can discover many interesting phenomena such as bank transactions, web page requesting sequences, customer behavior, etc. There have been many frequent itemset mining algorithms proposed so far, yet it is still a challenging task. In this paper, we propose a deep learning architecture for discovering closed sequences. The U-Net network is trained with random, synthetic sequences and, afterward, is able to discover unknown (not seen during training) sequences. The proposed solution is faster than traditional sequential data mining methods for longer sequences.