A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams

Nowadays the amount of networks of devices and sensors, such as smart homes or smart cities, is rapidly increasing. Each of these devices generates massive amounts of data on a continuous basis where an interpretable description of its state is interesting for the experts. This knowledge can be extr...

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Detalles Bibliográficos
Autores: García-Vico, Ángel M., Carmona, Cristóbal J., González, Pedro, del Jesus, María José
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2023
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/4303
Acceso en línea:https://doi.org/10.1016/j.inffus.2022.10.028
https://hdl.handle.net/10953/4303
Access Level:acceso abierto
Palabra clave:Data stream mining
Fuzzy rule-based systems
Distributed computing
Evolutionary fuzzy systems
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spelling A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streamsGarcía-Vico, Ángel M.Carmona, Cristóbal J.González, Pedrodel Jesus, María JoséData stream miningFuzzy rule-based systemsDistributed computingEvolutionary fuzzy systemsNowadays the amount of networks of devices and sensors, such as smart homes or smart cities, is rapidly increasing. Each of these devices generates massive amounts of data on a continuous basis where an interpretable description of its state is interesting for the experts. This knowledge can be extracted by means of emerging pattern mining techniques. In fact, it can be extracted locally on each device and joined together afterwards in order to obtain a global vision of the system without transferring any data. However, the traditional massive data processing frameworks are focused on the extraction of this global model, which produces huge amounts of data transfers throughout the network. This paper proposes a distributed method based on evolutionary fuzzy systems for both the extraction and subsequent fusion of descriptive emerging patterns in data streams from different sources of the same kind. First, an evolutionary algorithm following an informed approach for efficient data processing is presented for the extraction of emerging patterns on the data stream generated by each device, in order to obtain a local model for each stream. Then, several fusion methods are proposed for the aggregation of these patterns in order to extract the global model. A wide experimental study has been carried out to analyse the suitability of the evolutionary algorithm for the extraction of high-quality emerging patterns and its capacity to deal with concept drift. Finally, the quality of the proposed fusion methods is also analysed.Elsevier202520252023info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://doi.org/10.1016/j.inffus.2022.10.028https://hdl.handle.net/10953/4303reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésInformation Fusion 2023; Volume 91; Pages 412-423info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/43032026-06-24T12:41:07Z
dc.title.none.fl_str_mv A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
title A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
spellingShingle A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
García-Vico, Ángel M.
Data stream mining
Fuzzy rule-based systems
Distributed computing
Evolutionary fuzzy systems
title_short A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
title_full A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
title_fullStr A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
title_full_unstemmed A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
title_sort A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
dc.creator.none.fl_str_mv García-Vico, Ángel M.
Carmona, Cristóbal J.
González, Pedro
del Jesus, María José
author García-Vico, Ángel M.
author_facet García-Vico, Ángel M.
Carmona, Cristóbal J.
González, Pedro
del Jesus, María José
author_role author
author2 Carmona, Cristóbal J.
González, Pedro
del Jesus, María José
author2_role author
author
author
dc.subject.none.fl_str_mv Data stream mining
Fuzzy rule-based systems
Distributed computing
Evolutionary fuzzy systems
topic Data stream mining
Fuzzy rule-based systems
Distributed computing
Evolutionary fuzzy systems
description Nowadays the amount of networks of devices and sensors, such as smart homes or smart cities, is rapidly increasing. Each of these devices generates massive amounts of data on a continuous basis where an interpretable description of its state is interesting for the experts. This knowledge can be extracted by means of emerging pattern mining techniques. In fact, it can be extracted locally on each device and joined together afterwards in order to obtain a global vision of the system without transferring any data. However, the traditional massive data processing frameworks are focused on the extraction of this global model, which produces huge amounts of data transfers throughout the network. This paper proposes a distributed method based on evolutionary fuzzy systems for both the extraction and subsequent fusion of descriptive emerging patterns in data streams from different sources of the same kind. First, an evolutionary algorithm following an informed approach for efficient data processing is presented for the extraction of emerging patterns on the data stream generated by each device, in order to obtain a local model for each stream. Then, several fusion methods are proposed for the aggregation of these patterns in order to extract the global model. A wide experimental study has been carried out to analyse the suitability of the evolutionary algorithm for the extraction of high-quality emerging patterns and its capacity to deal with concept drift. Finally, the quality of the proposed fusion methods is also analysed.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.inffus.2022.10.028
https://hdl.handle.net/10953/4303
url https://doi.org/10.1016/j.inffus.2022.10.028
https://hdl.handle.net/10953/4303
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Information Fusion 2023; Volume 91; Pages 412-423
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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