A framework to monitor clusters evolution applied to economy and finance problems

Detalhes bibliográficos
Autor(a) principal: João Gama
Data de Publicação: 2012
Outros Autores: Márcia Barbosa Oliveira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/2520
http://dx.doi.org/10.3233/IDA-2011-0512
Resumo: The study of evolution has become an important research issue, especially in the last decade, due to our ability to collect and store high detailed and time-stamped data. The need for describing and understanding the behavior of a given phenomena over time led to the emergence of new frameworks and methods focused on the temporal evolution of data and models. In this paper we address the problem of monitoring the evolution of clusters over time and propose the MEC framework. MEC traces evolution through the detection and categorization of clusters transitions, such as births, deaths and merges, and enables their visualization through bipartite graphs. It includes a taxonomy of transitions, a tracking method based in the computation of conditional probabilities, and a transition detection algorithm. We use MEC with two main goals: to determine the general evolution trends and to detect abnormal behavior or rare events. To demonstrate the applicability of our framework we present real wo
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spelling A framework to monitor clusters evolution applied to economy and finance problemsThe study of evolution has become an important research issue, especially in the last decade, due to our ability to collect and store high detailed and time-stamped data. The need for describing and understanding the behavior of a given phenomena over time led to the emergence of new frameworks and methods focused on the temporal evolution of data and models. In this paper we address the problem of monitoring the evolution of clusters over time and propose the MEC framework. MEC traces evolution through the detection and categorization of clusters transitions, such as births, deaths and merges, and enables their visualization through bipartite graphs. It includes a taxonomy of transitions, a tracking method based in the computation of conditional probabilities, and a transition detection algorithm. We use MEC with two main goals: to determine the general evolution trends and to detect abnormal behavior or rare events. To demonstrate the applicability of our framework we present real wo2017-11-16T13:46:57Z2012-01-01T00:00:00Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/2520http://dx.doi.org/10.3233/IDA-2011-0512engJoão GamaMárcia Barbosa Oliveirainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:19:52Zoai:repositorio.inesctec.pt:123456789/2520Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:21.913315Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A framework to monitor clusters evolution applied to economy and finance problems
title A framework to monitor clusters evolution applied to economy and finance problems
spellingShingle A framework to monitor clusters evolution applied to economy and finance problems
João Gama
title_short A framework to monitor clusters evolution applied to economy and finance problems
title_full A framework to monitor clusters evolution applied to economy and finance problems
title_fullStr A framework to monitor clusters evolution applied to economy and finance problems
title_full_unstemmed A framework to monitor clusters evolution applied to economy and finance problems
title_sort A framework to monitor clusters evolution applied to economy and finance problems
author João Gama
author_facet João Gama
Márcia Barbosa Oliveira
author_role author
author2 Márcia Barbosa Oliveira
author2_role author
dc.contributor.author.fl_str_mv João Gama
Márcia Barbosa Oliveira
description The study of evolution has become an important research issue, especially in the last decade, due to our ability to collect and store high detailed and time-stamped data. The need for describing and understanding the behavior of a given phenomena over time led to the emergence of new frameworks and methods focused on the temporal evolution of data and models. In this paper we address the problem of monitoring the evolution of clusters over time and propose the MEC framework. MEC traces evolution through the detection and categorization of clusters transitions, such as births, deaths and merges, and enables their visualization through bipartite graphs. It includes a taxonomy of transitions, a tracking method based in the computation of conditional probabilities, and a transition detection algorithm. We use MEC with two main goals: to determine the general evolution trends and to detect abnormal behavior or rare events. To demonstrate the applicability of our framework we present real wo
publishDate 2012
dc.date.none.fl_str_mv 2012-01-01T00:00:00Z
2012
2017-11-16T13:46:57Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/2520
http://dx.doi.org/10.3233/IDA-2011-0512
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http://dx.doi.org/10.3233/IDA-2011-0512
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