Efficiently mining gapped and window constraint frequent sequential patterns

Hugo Alatrista-Salas, Agustin Guevara-Cogorno, Yoshitomi Maehara, Miguel Nunez-del-Prado

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)


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.

Idioma originalInglés
Título de la publicación alojadaModeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings
EditoresVicenc Torra, Yasuo Narukawa, Jordi Nin, Núria Agell
Número de páginas12
ISBN (versión impresa)9783030575236
EstadoPublicada - 2020
Publicado de forma externa
Evento17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020 - Sant Cugat del Vallès, Espana
Duración: 2 set. 20204 set. 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12256 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020
CiudadSant Cugat del Vallès


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