DME - Comunicações a Conferências / ConferenceItem
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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.
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Browsing DME - Comunicações a Conferências / ConferenceItem by Subject "Affinity Coefficient"
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- Classificação de dados de natureza complexa no contexto da Avaliação 360ºPublication . Sousa, Áurea; Batista, Maria da Graça Câmara; Medeiros, Marina do Couto; Bacelar-Nicolau, HelenaOs indivíduos no seu ambiente organizacional têm um papel preponderante nas empresas. Apresentam-se os principais resultados relativos à Avaliação 360º dos trabalhadores de uma empresa do setor da Medicina Dentária, a qual congrega a visão dos diferentes indivíduos envolvidos no trabalho de cada colaborador. Os resultados obtidos apontam para um desempenho satisfatório, permitiram identificar grupos / clusters de indivíduos associados a tipos de desempenho distintos e mostraram aspetos que poderão ser melhorados mediante a adoção de um conjunto de ações, no âmbito de uma intervenção estratégica fundada sobre o conhecimento, e que potenciem o melhoramento contínuo do desempenho de cada colaborador.
- Clustering of Symbolic Data based on Affinity Coefficient: Application to real data setsPublication . Sousa, Áurea; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Silva, OsvaldoThe increasing use of databases, often large ones, in diverse areas of study makes it pertinent to summarise data in terms of their most relevant concepts. These concepts may be described by types of complex data, also known as symbolic data […]. We present some results from the Ascendant Hierarchical Cluster Analysis (AHCA) of symbolic objects described by interval data, in order to illustrate the effectiveness of the Ascendent Hierarchical Cluster Analysis based on the weighted generalized affinity coefficient, for symbolic data. The measure of comparison between the elements was combined with classical aggregation criteria and probabilistic ones. The probabilistic aggregation criteria used in this study belong to a parametric family of methods in the scope of the probabilistic approach of AHCA, named VL methodology and the validation of the clustering results is based on some validation measures. Finally, we compare the results achieved by our approach with the ones obtained by other authors.
- Global approach for the comparison of Clustering ResultsPublication . Silva, Osvaldo; Bacelar-Nicolau, Helena; Nicolau, Fernando C.The extraction of useful knowledge from a Hierarchical Cluster Analysis (HCA) is a complex process which depends on many factors, such as the applied clustering algorithms and the strategies developed in the initial stage of the HCA. We present a global approach for evaluating the quality of clustering results based on the comparison of partitions from the different clustering algorithms using the most relevant information available (e.g. stability, isolation and homogeneity of the clusters). In addition, we suggest a visual method to facilitate the evaluation of the quality of the partitions that allows us a quick perception of the similarities and the differences between the partitions, including the behaviour of the elements in the partitions. We illustrate our approach using a real data set (horse data). We applied HCA based on the weighted generalized affinity coefficient (similarity coefficient) to the case of complex data (symbolic data), combined with 26 clustering (classic and probabilistic) algorithms. Finally, we discuss the obtained results and the contribution of this approach to a better knowledge on the cluster structure of a data set.
- 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.
- Symbolic Data Analysis for the Assessment of User Satisfaction: An Application to Reading Rooms Services.Publication . Sousa, Áurea; Tomás, Licínio Manuel Vicente; Silva, Osvaldo; Bacelar-Nicolau, HelenaThis paper re-examines and deepens the study of a portion of the data collected within the context of a wider 2007 research project conducted in the Autonomous Region of Azores. The 2007 study aimed to understand users’ habits, attitudes and cultural practices, concerning reading and utilization of different library services, archives and museums. Based upon knowledge that only data analysis of a representative sample can supply, the study aimed to identify the aspects that should be prioritized in a process of restructuring the cultural services of leisure and reading to be implemented. This paper, utilizing data from the 2007 study, presents some results from the Ascendant Hierarchical Cluster Analysis (AHCA) of symbolic objects, according to the treatment to which they were submitted. These objects are described by different symbolic attributes pertaining to the latent variable ‘Degree of Satisfaction’. This variable was evaluated according to different dimensions of on-the-spot reading and consultation services. The aggregation criteria used in this study belong to a parametric family of methods and the similarity measure used is the weighted generalized affinity coefficient, for symbolic data. The validation of the clustering results is based on some validation measures.