TY - GEN
T1 - Efficiently mining gapped and window constraint frequent sequential patterns
AU - Alatrista-Salas, Hugo
AU - Guevara-Cogorno, Agustin
AU - Maehara, Yoshitomi
AU - Nunez-del-Prado, Miguel
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Sequential pattern mining is one of the most widespread data mining tasks with several real-life decision-making applications. In this mining process, constraints were added to improve the mining efficiency for discovering patterns meeting specific user requirements. Therefore, the temporal constraints, in particular, those that arise from the implicit temporality of sequential patterns, will have the ability to efficiently apply temporary restrictions such as, window and gap constraints. In this paper, we propose a novel window and gap constrained algorithms based on the well-known PrefixSpan algorithm. For this purpose, we introduce the virtual multiplication operation aiming for a generalized window mining algorithm that preserves other constraints. We also extend the PrefixSpan Pseudo-Projection algorithm to mining patterns under the gap-constraint. Our performance study shows that these extensions have the same time complexity as PrefixSpan and good linear scalability.
AB - Sequential pattern mining is one of the most widespread data mining tasks with several real-life decision-making applications. In this mining process, constraints were added to improve the mining efficiency for discovering patterns meeting specific user requirements. Therefore, the temporal constraints, in particular, those that arise from the implicit temporality of sequential patterns, will have the ability to efficiently apply temporary restrictions such as, window and gap constraints. In this paper, we propose a novel window and gap constrained algorithms based on the well-known PrefixSpan algorithm. For this purpose, we introduce the virtual multiplication operation aiming for a generalized window mining algorithm that preserves other constraints. We also extend the PrefixSpan Pseudo-Projection algorithm to mining patterns under the gap-constraint. Our performance study shows that these extensions have the same time complexity as PrefixSpan and good linear scalability.
KW - Gap constraint
KW - Sequential pattern mining
KW - Temporal constraints
KW - Window constraint
UR - http://www.scopus.com/inward/record.url?scp=85090095433&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-57524-3_20
DO - 10.1007/978-3-030-57524-3_20
M3 - Conference contribution
AN - SCOPUS:85090095433
SN - 9783030575236
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 240
EP - 251
BT - Modeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings
A2 - Torra, Vicenc
A2 - Narukawa, Yasuo
A2 - Nin, Jordi
A2 - Agell, Núria
PB - Springer
T2 - 17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020
Y2 - 2 September 2020 through 4 September 2020
ER -