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dc.contributor.author Gutiérrez-Soto, Claudio
dc.contributor.author Galdames, Patricio
dc.contributor.author Palomino, Marco A.
dc.date.accessioned 2024-06-12T17:10:02Z
dc.date.available 2024-06-12T17:10:02Z
dc.date.issued 2024-06-03
dc.identifier.issn 2504-2289
dc.identifier.other Mendeley: 551db753-e5ac-308e-8304-2068aebf8838
dc.identifier.uri https://repositorio.uss.cl/handle/uss/8653
dc.description Publisher Copyright: © 2024 by the authors.
dc.description.abstract Deriving insight from data is a challenging task for researchers and practitioners, especially when working on spatio-temporal domains. If pattern searching is involved, the complications introduced by temporal data dimensions create additional obstacles, as traditional data mining techniques are insufficient to address spatio-temporal databases (STDBs). We hereby present a new algorithm, which we refer to as F1/FP, and can be described as a probabilistic version of the Minus-F1 algorithm to look for periodic patterns. To the best of our knowledge, no previous work has compared the most cited algorithms in the literature to look for periodic patterns—namely, Apriori, MS-Apriori, FP-Growth, Max-Subpattern, and PPA. Thus, we have carried out such comparisons and then evaluated our algorithm empirically using two datasets, showcasing its ability to handle different types of periodicity and data distributions. By conducting such a comprehensive comparative analysis, we have demonstrated that our newly proposed algorithm has a smaller complexity than the existing alternatives and speeds up the performance regardless of the size of the dataset. We expect our work to contribute greatly to the mining of astronomical data and the permanently growing online streams derived from social media en
dc.description.abstract Deriving insight from data is a challenging task for researchers and practitioners, especially when working on spatio-temporal domains. If pattern searching is involved, the complications introduced by temporal data dimensions create additional obstacles, as traditional data mining techniques are insufficient to address spatio-temporal databases (STDBs). We hereby present a new algorithm, which we refer to as F1/FP, and can be described as a probabilistic version of the Minus-F1 algorithm to look for periodic patterns. To the best of our knowledge, no previous work has compared the most cited algorithms in the literature to look for periodic patterns—namely, Apriori, MS-Apriori, FP-Growth, Max-Subpattern, and PPA. Thus, we have carried out such comparisons and then evaluated our algorithm empirically using two datasets, showcasing its ability to handle different types of periodicity and data distributions. By conducting such a comprehensive comparative analysis, we have demonstrated that our newly proposed algorithm has a smaller complexity than the existing alternatives and speeds up the performance regardless of the size of the dataset. We expect our work to contribute greatly to the mining of astronomical data and the permanently growing online streams derived from social media. es
dc.language.iso eng
dc.relation.ispartof vol. 8 Issue: no. 6 Pages: 1-19
dc.source Big Data and Cognitive Computing
dc.title An Efficient Probabilistic Algorithm to Detect Periodic Patterns in Spatio-Temporal Datasets en
dc.title.alternative Un Algoritmo Probabilístico Eficiente para Detectar Patrones Periódicos en Conjuntos de Datos Espacio-Temporales es
dc.type Artículo
dc.identifier.doi 10.3390/bdcc8060059
dc.publisher.department Facultad de Ingeniería, Arquitectura y Diseño

 

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