Can, Ali BurakUzun-Per, MeryemAktaş, Mehmet Sıddık2023-01-172023-01-172022Can, A. B., Uzun-Per, M. & Aktaş, M. S. (2022). A novel sequential pattern mining algorithm for large scale data sequences. Computational Science and Its Applications – ICCSA 2022 Workshops (pp. 698-708). Springer: Malaga, Spain. https://doi.org/10.1007/978-3-031-10536-497830311053579783031105364https://doi.org/10.1007/978-3-031-10536-4_46https://hdl.handle.net/20.500.13055/373Sequential pattern mining algorithms are unsupervised machine learning algorithms that allow finding sequential patterns on data sequences that have been put together based on a particular order. These algorithms are mostly optimized for finding sequential data sequences containing more than one element. Hence, we argue that there is a need for algorithms that are particularly optimized for data sequences that contain only one element. Within the scope of this research, we study the design and development of a novel algorithm that is optimized for data sets containing data sequences with single elements and that can detect sequential patterns with high performance. The time and memory requirements of the proposed algorithm are examined experimentally. The results show that the proposed algorithm has low running times, while it has the same accuracy results as the algorithms in the similar category in the literature. The obtained results are promising.eninfo:eu-repo/semantics/closedAccessSequential Pattern MiningGspPrefixspanLarge Scale Data SequencesMapreduce Programming ModelConference Object10.1007/978-3-031-10536-4_466987082-s2.0-85135954552Q2