Influencing factors on the buying behavior in the software market

The case of desktop Operating System and Office Suite choices considering Proprietary and Open Source Software alternatives.

Aplicação prática dos pilares da gestão numa fundação: melhoria de processos e procedimentos

Mestrado em Controlo de Gestão e dos Negócios

O controlo interno nos serviços e fundos autónomos

Mestrado em Auditoria

O conhecimento e inovação como factor de sucesso de uma organização : o caso Sport Lisboa e Benfica

Mestrado em Gestão e Empreendedorismo

Políticas e modelos de gestão em unidades de fisioterapia: estratégias para a mudança

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

O balanced scorecard aplicado a uma pme do setor audiovisual

Mestrado em Contabilidade e Gestão das Instituições

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion.