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Authors: Edgar Acuna 1 ; Velcy Palomino 2 ; José Agosto 3 ; Rémi Mégret 4 ; Tugrul Giray 3 ; Alberto Prado 5 ; Cédric Alaux 5 and Yves Le Conte 5

Affiliations: 1 Department of Mathematical Sciences, University of Puerto Rico, Mayaguez PR 00682 and U.S.A. ; 2 Program in Computing and Information Science and Engineering, University of Puerto Rico, Mayaguez PR 00682 and U.S.A. ; 3 Department of Biology, University of Puerto Rico, Rio Piedras, PR 00990 and U.S.A. ; 4 Department of Computer Science, University of Puerto Rico, Rio Piedras, PR 00990 and U.S.A. ; 5 INRA, UR406 Abeilles & Environnement, Site Agroparc, 84914 Avignon, France, UMT PrADE, Site Agroparc, 84914 Avignon and France

Keyword(s): Clustering, Honeybees Behavior, Data Wrangling, Time Series.

Related Ontology Subjects/Areas/Topics: Applications ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Multimedia ; Multimedia Signal Processing ; Pattern Recognition ; Telecommunications

Abstract: In this work, we analyze the activity of bees starting at 6 days old. The data was collected at the INRA (France) during 2014 and 2016. The activity is counted according to whether the bees enter or leave the hive. After data wrangling, we decided to analyze data corresponding to a period of 10 days. We use clustering method to determine bees with similar activity and to estimate the time during the day when the bees are most active. To achieve our objective, the data was analyzed in three different time periods in a day. One considering the daily activity during in two periods: morning and afternoon, then looking at activities in periods of 3 hours from 8:00am to 8:00pm and, finally looking at the activities hourly from 8:00am to 8:00pm. Our study found two clusters of bees and in one of them clearly the bees activity increased at the day 5. The smaller cluster included the most active bees representing about 24 percent of the total bees under study. Also, the highest activity of th e bees was registered between 2:00pm until 3:00pm. A Chi-square test shows that there is a combined effect Treatment× Colony on the clusters formation. (More)

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Paper citation in several formats:
Acuna, E., Palomino, V., Agosto, J., Mégret, R., Giray, T., Prado, A., Alaux, C. and Conte, Y. L. (2019). Clustering Honeybees by Its Daily Activity. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 598-604. DOI: 10.5220/0007387505980604

@conference{icpram19,
author={Edgar Acuna and Velcy Palomino and José Agosto and Rémi Mégret and Tugrul Giray and Alberto Prado and Cédric Alaux and Yves Le Conte},
title={Clustering Honeybees by Its Daily Activity},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2019},
pages={598-604},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007387505980604},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Clustering Honeybees by Its Daily Activity
SN - 978-989-758-351-3
IS - 2184-4313
AU - Acuna, E.
AU - Palomino, V.
AU - Agosto, J.
AU - Mégret, R.
AU - Giray, T.
AU - Prado, A.
AU - Alaux, C.
AU - Conte, Y.
PY - 2019
SP - 598
EP - 604
DO - 10.5220/0007387505980604
PB - SciTePress