Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques

Marcos Orellana; Jimmy Salto; Priscila Cedillo

doi:10.1007/978-3-030-73103-8_12

Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques

Marcos Orellana, Jimmy Salto, Priscila Cedillo

Universidad del Azuay

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

The relationship between atmospheric components and meteorological variables is essential to assess the air quality and thus avoid citizens' health risks. However, finding an association relationship between those factors could be complicated due to the number of categorization methods that can be used (e.g., frequency, size, binning). Therefore, the objective of this study is to propose a methodology that prepares data through a discretization process and then applies association techniques of the possible combinations between the analyzed variables. The results show that the method used is effective in locating patterns, which are useful for the environmental manager to find knowledge.

Idioma original	Inglés
Título de la publicación alojada	Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC
Editores	Kohei Arai
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	192-204
Número de páginas	13
ISBN (versión impresa)	9783030731021
DOI	https://doi.org/10.1007/978-3-030-73103-8_12
Estado	Publicada - 2021
Evento	Future of Information and Communication Conference, FICC 2021 - Virtual, Online Duración: 29 abr. 2021 → 30 abr. 2021

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1364 AISC
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	Future of Information and Communication Conference, FICC 2021
Ciudad	Virtual, Online
Período	29/04/21 → 30/04/21

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1007/978-3-030-73103-8_12

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Orellana, M., Salto, J., & Cedillo, P. (2021). Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques. En K. Arai (Ed.), Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC (pp. 192-204). (Advances in Intelligent Systems and Computing; Vol. 1364 AISC). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73103-8_12

Orellana, Marcos ; Salto, Jimmy ; Cedillo, Priscila. / Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques. Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. editor / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2021. pp. 192-204 (Advances in Intelligent Systems and Computing).

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title = "Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques",

abstract = "The relationship between atmospheric components and meteorological variables is essential to assess the air quality and thus avoid citizens' health risks. However, finding an association relationship between those factors could be complicated due to the number of categorization methods that can be used (e.g., frequency, size, binning). Therefore, the objective of this study is to propose a methodology that prepares data through a discretization process and then applies association techniques of the possible combinations between the analyzed variables. The results show that the method used is effective in locating patterns, which are useful for the environmental manager to find knowledge.",

keywords = "Association rules, Atmospheric pollutants, Data mining, Discretization, Meteorological variables",

author = "Marcos Orellana and Jimmy Salto and Priscila Cedillo",

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Orellana, M, Salto, J & Cedillo, P 2021, Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques. En K Arai (ed.), Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. Advances in Intelligent Systems and Computing, vol. 1364 AISC, Springer Science and Business Media Deutschland GmbH, pp. 192-204, Future of Information and Communication Conference, FICC 2021, Virtual, Online, 29/04/21. https://doi.org/10.1007/978-3-030-73103-8_12

Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques. / Orellana, Marcos; Salto, Jimmy; Cedillo, Priscila.
Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. ed. / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2021. p. 192-204 (Advances in Intelligent Systems and Computing; Vol. 1364 AISC).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques

AU - Orellana, Marcos

AU - Salto, Jimmy

AU - Cedillo, Priscila

PY - 2021

Y1 - 2021

N2 - The relationship between atmospheric components and meteorological variables is essential to assess the air quality and thus avoid citizens' health risks. However, finding an association relationship between those factors could be complicated due to the number of categorization methods that can be used (e.g., frequency, size, binning). Therefore, the objective of this study is to propose a methodology that prepares data through a discretization process and then applies association techniques of the possible combinations between the analyzed variables. The results show that the method used is effective in locating patterns, which are useful for the environmental manager to find knowledge.

AB - The relationship between atmospheric components and meteorological variables is essential to assess the air quality and thus avoid citizens' health risks. However, finding an association relationship between those factors could be complicated due to the number of categorization methods that can be used (e.g., frequency, size, binning). Therefore, the objective of this study is to propose a methodology that prepares data through a discretization process and then applies association techniques of the possible combinations between the analyzed variables. The results show that the method used is effective in locating patterns, which are useful for the environmental manager to find knowledge.

KW - Association rules

KW - Atmospheric pollutants

KW - Data mining

KW - Discretization

KW - Meteorological variables

UR - https://www.scopus.com/pages/publications/85105958883

U2 - 10.1007/978-3-030-73103-8_12

DO - 10.1007/978-3-030-73103-8_12

M3 - Contribución a la conferencia

AN - SCOPUS:85105958883

SN - 9783030731021

T3 - Advances in Intelligent Systems and Computing

SP - 192

EP - 204

BT - Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC

A2 - Arai, Kohei

PB - Springer Science and Business Media Deutschland GmbH

T2 - Future of Information and Communication Conference, FICC 2021

Y2 - 29 April 2021 through 30 April 2021

ER -

Orellana M, Salto J, Cedillo P. Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques. En Arai K, editor, Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. Springer Science and Business Media Deutschland GmbH. 2021. p. 192-204. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-73103-8_12

Behavior Analysis of Atmospheric Components and Meteorological Variables Applying Data Mining Association Techniques

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