Um algoritmo de vida artificial para agrupamento de dados variantes no tempo

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dc.contributor.advisorSilva, Leandro Nunes de Castropt_BR
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2741458816539568por
dc.contributor.authorSantos, Diego Gadens dospt_BR
dc.creator.Latteshttp://lattes.cnpq.br/5547804405395492por
dc.date.accessioned2016-03-15T19:37:44Z
dc.date.accessioned2020-05-28T18:08:33Z
dc.date.available2013-05-24pt_BR
dc.date.available2020-05-28T18:08:33Z
dc.date.issued2012-09-14pt_BR
dc.description.abstractCurrent technologies have made it possible to generate and store data in high volumes. To process and collect information in large databases is not always as easy as creating them. Therefore, this gap has stimulated the search for efficient techniques capable of extracting useful and non-trivial knowledge, which are intrinsic to these large data sets. The goal of this work is to propose a bioinspired algorithm, based on the Boids artificial life model, which will be used to group data in dynamic environments, i.e. in databases updated over time. The Bo-ids algorithm was originally created to illustrate the simulation of the coordinated movement observed in a flock of birds and other animals. Thus, to use this algorithm for data clustering, some modifications must be applied. These changes will be implemented in the classical rules of cohesion, separation and alignment of the Boids model in order to consider the distance (similarity/dissimilarity) among objects. Thus, it creates objects that stand and move around the space, representing the natural groups within the data, and it is expected that similar ob-jects tend to form dynamic clusters (groups) of Boids in the environment, while dissimilar objects tend to keep a larger distance between them. The results presented attest the robust-ness of the algorithm for clustering time-varying data under the light of different evaluation measures and in various databases from the literature.eng
dc.description.sponsorshipFundo Mackenzie de Pesquisapt_BR
dc.formatapplication/pdfpor
dc.identifier.citationSANTOS, Diego Gadens dos. Um algoritmo de vida artificial para agrupamento de dados variantes no tempo. 2012. 92 f. Dissertação (Mestrado em Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2012.por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/24343
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.subjectvida artificialpor
dc.subjectcomputação naturalpor
dc.subjectmineração de dadospor
dc.subjectagrupamento de dadospor
dc.subjectboidspor
dc.subjectdados variantes no tempopor
dc.subjectartificial lifeeng
dc.subjectnatural computingeng
dc.subjectdata miningeng
dc.subjectdata clusteringeng
dc.subjectboidseng
dc.subjecttime-varying dataeng
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA ELETRICApor
dc.thumbnail.urlhttp://tede.mackenzie.br/jspui/retrieve/3827/Diego%20Gadens%20dos%20Santos.pdf.jpg*
dc.titleUm algoritmo de vida artificial para agrupamento de dados variantes no tempopor
dc.typeDissertaçãopor
local.contributor.board1Silva, Leandro Augusto dapt_BR
local.contributor.board1Latteshttp://lattes.cnpq.br/1396385111251741por
local.contributor.board2Zuben, Fernando José Vonpt_BR
local.contributor.board2Latteshttp://lattes.cnpq.br/1756895777404187por
local.publisher.countryBRpor
local.publisher.departmentEngenharia Elétricapor
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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