Agrupamento de dados superparamagnético

Detalhes bibliográficos
Ano de defesa: 2009
Autor(a) principal: ALMEIDA, Evert Elvis Batista de lattes
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:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4977
Resumo: 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.