CICS/A - Comunicações a Conferências / ConferenceItem
Permanent URI for this collection
Todo o tipo de documentos relacionados com uma conferência; ex.: artigos de conferências, relatórios de conferências, palestras em conferências, artigos publicados em proceedings de conferências, relatórios de abstracts de artigos de conferência e posters de conferências.
(Aceite; Publicado; Actualizado).
Browse
Browsing CICS/A - Comunicações a Conferências / ConferenceItem by Author "Bacelar-Nicolau, Helena"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
- Classes de objectos simbólicos: dados da indústria automóvelPublication . Sousa, Áurea; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Silva, OsvaldoNeste trabalho, é abordada a Análise Classificatória Hierárquica Ascendente (ACHA) de dados simbólicos ou complexos (generalizações de dados clássicos), com base no coeficiente de afinidade generalizado ponderado e em critérios de agregação clássicos e probabilísticos, estes últimos no âmbito da metodologia VL. São apresentados os principais resultados obtidos com a ACHA de 33 modelos de carros (dados simbólicos na área da indústria automóvel), com base no coeficiente de afinidade generalizado ponderado, centrado e reduzido pelo método de Wald e Wolfowitz, comparando-se os resultados obtidos com os de outros autores e com a partição definida a priori pelas categorias ("Utilitário", "Berlina", "Desportivo", "Luxo") a que os modelos de carros pertencem.
- Entrepreneurship Education : The role of the Higher Education Institutions in the Entrepreneurial Attitudes of the StudentsPublication . Sousa, Áurea; Couto, Gualter; Branco, Nélia Cavaco; Silva, Osvaldo; Bacelar-Nicolau, HelenaThe entrepreneurship education has been taking an increasing importance in the educational programs, emphasizing the role of the higher education institutions as promoters of the entrepreneurship. In this work, which is based on the responses obtained through a survey conducted with a sample consisting of 305 students of the Azores University, enrolled in courses of different scientific areas, we aim to assess how, in the student’s perspective, the university can stimulate the interest of their students in creating business. From hierarchical clustering methods we obtain a typology of variables linked to initiatives and activities that could be developed by the university. The main results obtained from some non-parametric hypothesis tests and from correspondence analysis, simple and multiple, are also presented.
- Hierarchical cluster analysis of groups of individuals : application to business dataPublication . Sousa, Áurea; Bacelar-Nicolau, Helena; Silva, OsvaldoWe present one example, in which the data are issued from a questionnaire in order to find satisfaction typologies (with the services provided by an automobile company) of independent groups of individuals. The Agglomerative Hierarchical Cluster Analysis (AHCA) was based on two approaches: one based on a particular case of the generalized weighted affinity coefficient, which deals with classical data, and the other one on the weighted generalized affinity coefficient for the case of symbolic/complex data. Both measures of comparison between elements were combined with classical and probabilistic aggregation criteria. We used the global statistics of levels (STAT) to evaluate the quality of the obtained partitions.
- Probabilistic approach for comparing partitionsPublication . Silva, Osvaldo; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, ÁureaThe comparison of two partitions in Cluster Analysis can be performed using various classical coefficients (or indexes) in the context of three approaches (based, respectively, on the count of pairs, on the pairing of the classes and on the variation of information). However, different indexes usually highlight different peculiarities of the partitions to compare. Moreover, these coefficients may have different variation ranges or they do not vary in the predicted interval, but rather only in one of their subintervals. Furthermore, there is a great diversity of validation techniques capable of assisting in the choice of the best partitioning of the elements to be classified, but in general each one tends to favour a certain kind of algorithm. Thus, it is useful to find ways to compare the results obtained using different approaches. In order to assist this assessment, a probabilistic approach to comparing partitions is presented and exemplified. This approach, based on the VL (Validity Linkage) Similarity, has the advantage, among others, of standardizing the measurement scales in a unique probabilistic scale. In this work, the partitions obtained from the agglomerative hierarchical cluster analysis of a dataset in the field of teaching are evaluated using classical and probabilistic (of VL type) indexes, and the obtained results are compared.