@Article{PereiraSilGraCoeCec:2019:StReAm,
author = "Pereira, J{\'e}ssica de Farias and Silva, Ana Estela Antunes da
and Gradvohl, Andr{\'e} Leon Sampaio and Coelho, Guilherme
Palermo and Cecatto, Jos{\'e} Roberto",
affiliation = "{Universidade Estadual de Campinas (UNICAMP)} and {Universidade
Estadual de Campinas (UNICAMP)} and {Universidade Estadual de
Campinas (UNICAMP)} and {Universidade Estadual de Campinas
(UNICAMP)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "A study of the relationship among parameters of M/X solar flares
via association rules",
journal = "International Journal of Artificial Intelligence and Expert
Systems",
year = "2019",
volume = "8",
number = "4",
pages = "63--77",
keywords = "Solar Flares, Data Mining, Association Rules.",
abstract = "This paper introduces a method to study the relation among
parameters that can cause the origin of M/X solar flares. Solar
flares, especially flares of types M and X, make the Earths
atmosphere more ionized and have an effect on radio signals, which
can cause disruptions in wireless communications. This situation
points out to the need for better identification of the parameters
involved in M/X solar flares. The method is based on four
categorical parameters and their relations. Relations are
demonstrated by association rules which were extracted by the
APRIORI algorithm and the most promising rules were filtered by
support and confidence metrics. Results of the most promising
rules had been compared by application to different periods of the
23rd and the 24th solar cycles.",
issn = "2180-124X",
language = "en",
targetfile = "pereira_study.pdf",
urlaccessdate = "23 set. 2024"
}