Agrupamento de dados superparamagnético

• Ano de defesa: 2009
• Autor(a) principal: ALMEIDA, Evert Elvis Batista de
• Orientador(a): SOUZA, Adauto José Ferreira de
• Banca de defesa: CAMPOS, Paulo Roberto de Araújo, STOSIC, Tatijana, REN, Tsang Ing
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal Rural de Pernambuco
• Programa de Pós-Graduação: Programa de Pós-Graduação em Biometria e Estatística Aplicada
• Departamento: Departamento de Estatística e Informática
• País: Brasil
• Palavras-chave em Português: Agrupamento de dados; Reconhecimento de padrões; Simulação no ensemble microcanônico
• Palavras-chave em Inglês: Data clustering; Pattern recognition; Microcanonical ensemble simulation
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
• Link de acesso: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4977

We applied a non-supervisioned data clustering technique based on a map of the problem into an inhomogeneous granular magnet problem. The physical behavior of the magnet is studied through the usual Monte Carlo method. Each data item is described by a set of numerical attributes, interpreted as points in a multiple-dimensional Euclidian space. The mapping consists in associating a Potts spin to each data point. The physical system is described by a disordered Potts Hamiltonian with several states with an exponentially decaying interaction among spins. The magnet reaches a superparamagnetic state at high temperatures in which the spins in certain grains are strongly correlated whereas the grains are loosely linked. In this way, each grain corresponds to a group or cluster. We implemented the method in a microcanonical ensemble where the conserved total energy is the control parameter. The temperature is calculated during the simulation and, besides thermodynamic stable states, it is possible to sample unstable and metastable state as well. We work with three artificial multiple-dimensional data set and a four-dimensional real data set. We obtained good results in all cases and discuss some issues concerning the microcanonical implementation of the superparamagnetic data clustering.