Determining shoal membership using affinity propagation

► We propose using the affinity propagation clustering for detecting multiple shoals. ► A soft temporal constraint is included in order to detect shoal fusion and fission. ► We explore how affinity propagation performs on agent-based simulated shoals. ► We compare affinity propagation clustering to...

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Published in:	Behavioural brain research Vol. 241; pp. 38 - 49
Main Authors:	Quera, Vicenç, Beltran, Francesc S., Givoni, Inmar E., Dolado, Ruth
Format:	Journal Article
Language:	English
Published:	Shannon Elsevier B.V 15.03.2013 Elsevier
Subjects:	Adaptació animal Adult Affinity propagation clustering Algorithms Animal adaptation Animal group membership Behavioral psychophysiology Biological and medical sciences Cluster Analysis Comparative psychology Computer Simulation Female Fundamental and applied biological sciences. Psychology Group Processes Human clustering validation Humans Male Psicologia comparada Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Shoal fusion and fission Shoal membership Soft temporal constraint Shoal membership Shoal fusion and fission Soft temporal constraint Affinity propagation clustering Animal group membership Human clustering validation Human Validation Propagation Social group Social belonging Animal Affinity Social identity
ISSN:	0166-4328, 1872-7549, 1872-7549
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Abstract	► We propose using the affinity propagation clustering for detecting multiple shoals. ► A soft temporal constraint is included in order to detect shoal fusion and fission. ► We explore how affinity propagation performs on agent-based simulated shoals. ► We compare affinity propagation clustering to human clustering of the same data. ► Affinity propagation is an appealing approach for detecting shoal dynamics. We propose using the affinity propagation (AP) clustering algorithm for detecting multiple disjoint shoals, and we present an extension of AP, denoted by STAP, that can be applied to shoals that fusion and fission across time. STAP incorporates into AP a soft temporal constraint that takes cluster dynamics into account, encouraging partitions obtained at successive time steps to be consistent with each other. We explore how STAP performs under different settings of its parameters (strength of the temporal constraint, preferences, and distance metric) by applying the algorithm to simulated sequences of collective coordinated motion. We study the validity of STAP by comparing its results to partitioning of the same data obtained from human observers in a controlled experiment. We observe that, under specific circumstances, AP yields partitions that agree quite closely with the ones made by human observers. We conclude that using the STAP algorithm with appropriate parameter settings is an appealing approach for detecting shoal fusion–fission dynamics.
AbstractList	► We propose using the affinity propagation clustering for detecting multiple shoals. ► A soft temporal constraint is included in order to detect shoal fusion and fission. ► We explore how affinity propagation performs on agent-based simulated shoals. ► We compare affinity propagation clustering to human clustering of the same data. ► Affinity propagation is an appealing approach for detecting shoal dynamics. We propose using the affinity propagation (AP) clustering algorithm for detecting multiple disjoint shoals, and we present an extension of AP, denoted by STAP, that can be applied to shoals that fusion and fission across time. STAP incorporates into AP a soft temporal constraint that takes cluster dynamics into account, encouraging partitions obtained at successive time steps to be consistent with each other. We explore how STAP performs under different settings of its parameters (strength of the temporal constraint, preferences, and distance metric) by applying the algorithm to simulated sequences of collective coordinated motion. We study the validity of STAP by comparing its results to partitioning of the same data obtained from human observers in a controlled experiment. We observe that, under specific circumstances, AP yields partitions that agree quite closely with the ones made by human observers. We conclude that using the STAP algorithm with appropriate parameter settings is an appealing approach for detecting shoal fusion–fission dynamics. We propose using the affinity propagation (AP) clustering algorithm for detecting multiple disjoint shoals, and we present an extension of AP, denoted by STAP, that can be applied to shoals that fusion and fission across time. STAP incorporates into AP a soft temporal constraint that takes cluster dynamics into account, encouraging partitions obtained at successive time steps to be consistent with each other. We explore how STAP performs under different settings of its parameters (strength of the temporal constraint, preferences, and distance metric) by applying the algorithm to simulated sequences of collective coordinated motion. We study the validity of STAP by comparing its results to partitioning of the same data obtained from human observers in a controlled experiment. We observe that, under specific circumstances, AP yields partitions that agree quite closely with the ones made by human observers. We conclude that using the STAP algorithm with appropriate parameter settings is an appealing approach for detecting shoal fusion-fission dynamics. We propose using the affinity propagation (AP) clustering algorithm for detecting multiple disjoint shoals, and we present an extension of AP, denoted by STAP, that can be applied to shoals that fusion and fission across time. STAP incorporates into AP a soft temporal constraint that takes cluster dynamics into account, encouraging partitions obtained at successive time steps to be consistent with each other. We explore how STAP performs under different settings of its parameters (strength of the temporal constraint, preferences, and distance metric) by applying the algorithm to simulated sequences of collective coordinated motion. We study the validity of STAP by comparing its results to partitioning of the same data obtained from human observers in a controlled experiment. We observe that, under specific circumstances, AP yields partitions that agree quite closely with the ones made by human observers. We conclude that using the STAP algorithm with appropriate parameter settings is an appealing approach for detecting shoal fusion-fission dynamics.We propose using the affinity propagation (AP) clustering algorithm for detecting multiple disjoint shoals, and we present an extension of AP, denoted by STAP, that can be applied to shoals that fusion and fission across time. STAP incorporates into AP a soft temporal constraint that takes cluster dynamics into account, encouraging partitions obtained at successive time steps to be consistent with each other. We explore how STAP performs under different settings of its parameters (strength of the temporal constraint, preferences, and distance metric) by applying the algorithm to simulated sequences of collective coordinated motion. We study the validity of STAP by comparing its results to partitioning of the same data obtained from human observers in a controlled experiment. We observe that, under specific circumstances, AP yields partitions that agree quite closely with the ones made by human observers. We conclude that using the STAP algorithm with appropriate parameter settings is an appealing approach for detecting shoal fusion-fission dynamics.
Author	Beltran, Francesc S. Dolado, Ruth Quera, Vicenç Givoni, Inmar E.
Author_xml	– sequence: 1 givenname: Vicenç surname: Quera fullname: Quera, Vicenç email: vquera@ub.edu organization: Institute for Brain, Cognition and Behavior (IR3C), Adaptive Behavior and Interaction Research Group (GCAI), Department of Behavioral Science Methods, University of Barcelona, Campus Mundet, Passeig Vall d’Hebron 171, 08035 Barcelona, Spain – sequence: 2 givenname: Francesc S. surname: Beltran fullname: Beltran, Francesc S. email: francesc.salvador@ub.edu organization: Institute for Brain, Cognition and Behavior (IR3C), Adaptive Behavior and Interaction Research Group (GCAI), Department of Behavioral Science Methods, University of Barcelona, Campus Mundet, Passeig Vall d’Hebron 171, 08035 Barcelona, Spain – sequence: 3 givenname: Inmar E. surname: Givoni fullname: Givoni, Inmar E. email: inmar@psi.utoronto.ca organization: Department of Computer Science, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada – sequence: 4 givenname: Ruth surname: Dolado fullname: Dolado, Ruth email: ruth.dolado@ub.edu organization: Institute for Brain, Cognition and Behavior (IR3C), Adaptive Behavior and Interaction Research Group (GCAI), Department of Behavioral Science Methods, University of Barcelona, Campus Mundet, Passeig Vall d’Hebron 171, 08035 Barcelona, Spain
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Keywords	Shoal membership Shoal fusion and fission Soft temporal constraint Affinity propagation clustering Animal group membership Human clustering validation Human Validation Propagation Social group Social belonging Animal Affinity Social identity
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Snippet	► We propose using the affinity propagation clustering for detecting multiple shoals. ► A soft temporal constraint is included in order to detect shoal fusion... We propose using the affinity propagation (AP) clustering algorithm for detecting multiple disjoint shoals, and we present an extension of AP, denoted by STAP,...
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SubjectTerms	Adaptació animal Adult Affinity propagation clustering Algorithms Animal adaptation Animal group membership Behavioral psychophysiology Biological and medical sciences Cluster Analysis Comparative psychology Computer Simulation Female Fundamental and applied biological sciences. Psychology Group Processes Human clustering validation Humans Male Psicologia comparada Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Shoal fusion and fission Shoal membership Soft temporal constraint
Title	Determining shoal membership using affinity propagation
URI	https://dx.doi.org/10.1016/j.bbr.2012.11.031 https://www.ncbi.nlm.nih.gov/pubmed/23219963 https://www.proquest.com/docview/1273779402 https://www.proquest.com/docview/1285102387 https://recercat.cat/handle/2072/209918
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