Browsing by Author "Bacelar-Nicolau, Leonor"
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- Clustering of variables with a three-way approach for health sciencesPublication . Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, Áurea; Bacelar-Nicolau, LeonorCluster analysis or classification usually concerns a set of exploratory multivariate data analysis methods and techniques for grouping either a set of statistical data units or the associated set of descriptive variables, into clusters of similar and, hopefully, well separated elements. In this work we refer to an extension of this paradigm to generalized three-way data representations and particularly to classification of interval variables. Such approach appears to be especially useful in large data bases, mostly in a data mining context. A health sciences case study is partially discussed.
- Do Univariado ao Multivariado : A Escala de Elementos Tangíveis, suas Relações com Outras Escalas e Mais AlémPublication . Bacelar-Nicolau, Helena; Sousa, Áurea; Bacelar-Nicolau, Leonor; Marques, M. SilvérioAnálise da qualidade e da satisfação dos doentes com apoio domiciliário, no âmbito do Projecto de Humanização dos Cuidados Paliativos em Contexto Domiciliário (Projecto SDH.MD/P.I.01.13; subsidiado pela Fundação Calouste Gulbenkian). Utilizou-se o Questionário SERVQUAL Modificado. O Questionário SERVQUAL Modificado é constituído por três blocos: Bloco 1 - Escala de Percepções, Bloco 2 - Escala de Preferências e por um conjunto de questões relativas a Dados do Doente e a Dados do Cuidador- Bloco 3. Referir-nos-emos nesta Parte 5.3 essencialmente à análise e a resultados respeitantes ao Bloco 1 da Escala de Percepções e respectivos itens, bem como suas relações com alguns itens do Bloco 3. […].
- Measuring similarity of complex and heterogeneous data in clustering of large data setsPublication . Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, Áurea; Bacelar-Nicolau, LeonorCluster analysis or classification usually concerns a set of exploratory multivariate data analysis methods and techniques for finding a clustering structure on a dataset. That may refer either to groups of statistical data units or to groups of variables. In this work we deal with a generalization of this paradigm concerning clustering of complex data described by three different types of variables, frequently present in a three-way context. We obtain compatible versions of the same affinity coefficient for measuring similarity between statistical data units described by those three types of variables. A global generalized similarity coefficient is analyzed for such kind of mixed data, often arising in data mining or knowledge mining.
- On cluster analysis of complex and heterogeneous dataPublication . Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, Áurea; Bacelar-Nicolau, LeonorCluster analysis or "unsupervised" classification (from "unsupervised learning", in pattern recognition literature) usually concerns a set of exploratory multivariate data analysis methods and techniques for grouping either statistical data units or variables into groups of similar elements, that is finding a clustering structure in the data. Classical clustering methods usually work with a set of objects as statistical data units described by a set of homogeneous (that is, of the same type) variables in a two-way framework. This paradigm can be extended in such way that data units may be either simple / first-order elements (e.g., objects, subjects, cases) or groups of / second-order or more elements from a population (e.g., subsets, samples, classes of a partition) and/or descriptive variables may simultaneously be of different (e.g., binary, multi-valued, histogram or interval) types. Therefore, one has a complex and/or heterogeneous data set under analysis. In that case classification will often be carried out by using a three-way or a symbolic/complex approach. The present work synthesizes previous methodological results and shows several developments mostly regarding hierarchical cluster analysis of complex data, where statistical data units are described by either a homogeneous or a heterogeneous set of variables. We will illustrate that approach on a case study issued from the statistical literature. The methodology has been applied with success in a data mining context, concerning multivariate analysis of real-life data bases from economy, management, medicine, education and social sciences.