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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Comit?s de grupamento aplicados a dados de express?o g?nica

Silva, Shirlly Christiany Macedo 20 January 2006 (has links)
Made available in DSpace on 2014-12-17T15:47:57Z (GMT). No. of bitstreams: 1 ShirllyCMS.pdf: 557194 bytes, checksum: 9adadd98c97ef1f0b498b06d2051e869 (MD5) Previous issue date: 2006-01-20 / The main goal of this work is to investigate the suitability of applying cluster ensemble techniques (ensembles or committees) to gene expression data. More specifically, we will develop experiments with three diferent cluster ensembles methods, which have been used in many works in literature: coassociation matrix, relabeling and voting, and ensembles based on graph partitioning. The inputs for these methods will be the partitions generated by three clustering algorithms, representing diferent paradigms: kmeans, ExpectationMaximization (EM), and hierarchical method with average linkage. These algorithms have been widely applied to gene expression data. In general, the results obtained with our experiments indicate that the cluster ensemble methods present a better performance when compared to the individual techniques. This happens mainly for the heterogeneous ensembles, that is, ensembles built with base partitions generated with diferent clustering algorithms / O principal objetivo deste trabalho ? investigar a viabilidade da aplica??o de t?cnicas de combina??o de agrupamentos (comit?s de agrupamento) a dados de express?o g?nica. Mais especificamente, ser?o realizados experimentos com tr?s m?todos diferentes de comit?s de agrupamentos que v?m sendo bastante usados na literatura: matriz de coassocia??o, rerotulagem e vota?ao, e comit?s baseados em particiona mento de grafo. A entrada para esses m?todos de combina??o ser?o as parti??es geradas por tr?s algoritmos de agrupamento, os quais representam diferentes paradigmas: arquico com liga??o k m?dias, ExpectationMaximization (EM), e o algoritmo hier?rquico com liga??o m?dia. Todos esse algoritmos v?m sendo amplamente utilizados no contexto de dados de express?o g?nica. De forma geral, os resultados obtidos indicam um desempenho superior das t?cnicas de comit?s em rela??o as t?cnicas de agrupamento individuais, principalmente no contexto de comit?s heterog?neos, isto ?, comit?s formados por parti??es base geradas por diferentes algoritmos de agrupamentos

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