000061525 001__ 61525
000061525 005__ 20200221144341.0
000061525 0247_ $$2doi$$a10.1016/j.ins.2016.01.063
000061525 0248_ $$2sideral$$a94026
000061525 037__ $$aART-2016-94026
000061525 041__ $$aeng
000061525 100__ $$0(orcid)0000-0002-2726-6760$$aGarcía-Magariño, I.$$uUniversidad de Zaragoza
000061525 245__ $$aA hybrid approach with agent-based simulation and clustering for sociograms
000061525 260__ $$c2016
000061525 5060_ $$aAccess copy available to the general public$$fUnrestricted
000061525 5203_ $$aIn the last years, some features of sociograms have proven to be strongly related to the performance of groups. However, the prediction of sociograms according to the features of individuals is still an open issue. In particular, the current approach presents a hybrid approach between agent-based simulation and clustering for simulating sociograms according to the psychological features of their members. This approach performs the clustering extracting certain types of individuals regarding their psychological characteristics, from training data. New people can then be associated with one of the types in order to run a sociogram simulation. This approach has been implemented with the tool called CLUS-SOCI (an agent-based and CLUStering tool for simulating SOCIograms). The current approach has been experienced with real data from four different secondary schools, with 38 real sociograms involving 714 students. Two thirds of these data were used for training the tool, while the remaining third was used for validating it. In the validation data, the resulting simulated sociograms were similar to the real ones in terms of cohesion, coherence of reciprocal relations and intensity, according to the binomial test with the correction of Bonferroni.
000061525 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T81
000061525 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000061525 590__ $$a4.832$$b2016
000061525 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b7 / 146 = 0.048$$c2016$$dQ1$$eT1
000061525 592__ $$a1.78$$b2016
000061525 593__ $$aArtificial Intelligence$$c2016$$dQ1
000061525 593__ $$aComputer Science Applications$$c2016$$dQ1
000061525 593__ $$aTheoretical Computer Science$$c2016$$dQ1
000061525 593__ $$aInformation Systems and Management$$c2016$$dQ1
000061525 593__ $$aSoftware$$c2016$$dQ1
000061525 593__ $$aControl and Systems Engineering$$c2016$$dQ1
000061525 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000061525 700__ $$0(orcid)0000-0001-7671-7540$$aMedrano, C.$$uUniversidad de Zaragoza
000061525 700__ $$0(orcid)0000-0003-3492-7544$$aLombas, A.S.$$uUniversidad de Zaragoza
000061525 700__ $$0(orcid)0000-0001-9320-1888$$aBarrasa, A.$$uUniversidad de Zaragoza
000061525 7102_ $$14009$$2740$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Psicología Social
000061525 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000061525 7102_ $$14009$$2620$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Metod.Ciencias Comportam.
000061525 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000061525 773__ $$g345 (2016), 81-95$$pInf. sci.$$tInformation Sciences$$x0020-0255
000061525 8564_ $$s1477993$$uhttps://zaguan.unizar.es/record/61525/files/texto_completo.pdf$$yPreprint
000061525 8564_ $$s70131$$uhttps://zaguan.unizar.es/record/61525/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
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000061525 951__ $$a2020-02-21-13:50:25
000061525 980__ $$aARTICLE