TY - GEN
T1 - K-Means clustering for information dissemination of fishing surveillance
AU - Correia, Anacleto
AU - Moura, Ricardo
AU - Água, Pedro
AU - Lobo, Victor
N1 - © 2020 Springer Nature Switzerland AG
PY - 2020
Y1 - 2020
N2 - The Portuguese Navy is responsible for monitoring the largest Exclusive Economic Zone in Europe. The most captured species in this area are Scomber colias and Trachurus trachurus, commonly called Mackerel and Horse Mackerel, respectively. One of the Navy’s missions is pursuing actions of fishing surveillance to verify the compliance of proceedings with the species’ fishing activity regulation. This monitoring actions originate data that represents a sample of the fishing activity in the area. The collected data, analysed with adequate data mining techniques, makes it possible to extract useful information to better understand the fishing activity related to Mackerel and Horse Mackerel, even if the full data set cannot be disclosed. With this in mind the authors used a non-supervised learning technique, the K-Means algorithm, which grouped data in clusters by its similarity and made a summarized description of each cluster with the purpose of releasing a general overview of such records. The information obtained from the clusters led the authors to deepen the study by performing a comparison of the monthly average quantity recorded per vessel for the two species in order to infer about the relation between captured quantity Mackerel and Horse Mackerel over time.
AB - The Portuguese Navy is responsible for monitoring the largest Exclusive Economic Zone in Europe. The most captured species in this area are Scomber colias and Trachurus trachurus, commonly called Mackerel and Horse Mackerel, respectively. One of the Navy’s missions is pursuing actions of fishing surveillance to verify the compliance of proceedings with the species’ fishing activity regulation. This monitoring actions originate data that represents a sample of the fishing activity in the area. The collected data, analysed with adequate data mining techniques, makes it possible to extract useful information to better understand the fishing activity related to Mackerel and Horse Mackerel, even if the full data set cannot be disclosed. With this in mind the authors used a non-supervised learning technique, the K-Means algorithm, which grouped data in clusters by its similarity and made a summarized description of each cluster with the purpose of releasing a general overview of such records. The information obtained from the clusters led the authors to deepen the study by performing a comparison of the monthly average quantity recorded per vessel for the two species in order to infer about the relation between captured quantity Mackerel and Horse Mackerel over time.
KW - Fishery surveillance
KW - Geo-spatial data
KW - K-Means clustering
UR - http://www.scopus.com/inward/record.url?scp=85080930597&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-40690-5_9
DO - 10.1007/978-3-030-40690-5_9
M3 - Conference contribution
AN - SCOPUS:85080930597
SN - 978-3-030-40689-9
T3 - Advances in Intelligent Systems and Computing
SP - 84
EP - 93
BT - Information Technology and Systems - Proceedings of ICITS 2020
A2 - Rocha, Álvaro
A2 - Ferrás, Carlos
A2 - Montenegro Marin, Carlos Enrique
A2 - Medina García, Víctor Hugo
PB - Springer
CY - Cham
T2 - International Conference on Information Technology and Systems, ICITS 2020
Y2 - 5 February 2020 through 7 February 2020
ER -