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|Título:||On cluster analysis of complex and heterogeneous data|
Nicolau, Fernando C.
Hierarchical Clustering Model
|Editora:||ISAST - International Society for the Advancement of Science and Technology|
|Citação:||Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, Áurea; Bacelar-Nicolau, Leonor (2014). "On cluster analysis of complex and heterogeneous data". Proceedings of the 3rd Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2014), C. H. Skiadas (Eds.), 2014 ISAST, 99-108|
|Resumo:||Cluster 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.|
|Descrição:||Proceedings of the 3rd Stochastic Modeling Techniques and Data Analysis International Conference, 11-14 June 2014, Lisbon, Portugal|
© 2014 ISAST
|Versão do Editor:||http://www.smtda.net/images/1_A-F_SMTDA2014_Proceedings_NEW.pdf|
|Aparece nas colecções:||FM-IMP-Comunicações|
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