A novel sequential pattern mining algorithm for large scale data sequences
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Sequential 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.
Açıklama
Anahtar Kelimeler
Sequential Pattern Mining, Gsp, Prefixspan, Large Scale Data Sequences, Mapreduce Programming Model
Kaynak
Computational Science and Its Applications – ICCSA 2022 Workshops
WoS Q Değeri
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
Can, 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/10.1007/978-3-031-10536-4
