# Paula Brito

• Statistical Analysis and Data Mining
• 2011
This paper introduces symbolic data analysis, explaining how it extends the classical data models to take into account more complete and complex information. Several examples motivate the approach, before the modeling of variables assuming new types of realizations are formally presented. Some methods for the (multivariate) analysis of symbolic data are(More)
In this paper we discuss some issues which arise when applying classical data analysis techniques to interval data, focusing on the notions of dispersion, association and linear combinations of interval variables. We present some methods that have been proposed for analysing this kind of data, namely for clustering, discriminant analysis, linear regression(More)
• CLA
• 2011
This work comes within the field of data analysis using Galois lattices. We consider ordinal, numerical single or interval data as well as data that consist on frequency/probability distributions on a finite set of categories. Data are represented and dealt with on a common framework, by defining a generalization operator that determines intents by(More)
• Pattern Recognition
• 2006
Applying graph theory to clustering, we propose a partitional clustering method and a clustering tendency index. No initial assumptions about the data set are requested by the method. The number of clusters and the partition that best fits the data set, are selected according to the optimal clustering tendency index value. 2005 Pattern Recognition Society.(More)
This paper presents a method for clustering a set of symbolic data where individuals are described by symbolic variables of various types: interval, categorical multi-valued or modal variables, which take into account the variability or uncertainty present in the data. Hierarchical and pyramidal clustering models are considered. The constructed clusters(More)
• ICPRAM
• 2012
We consider some classically based methods for fitting a multiple regression model to intervalvalued data (de Carvalho et al., 2004; Lima Neto et al., 2005; Lima Neto and de Carvalho, 2010). Then, a so-called symbolic model is fitted where now the regression parameters are estimated by using the symbolic sample covariance and variance functions of Billard(More)
• EGC
• 2012
Résumé. Nous nous intéressons aux méthodes de classification hiérarchique ou pyramidale, où chaque classe formée correspond à un concept, i.e. une paire (extension, intension), considérant des données décrites par des variables quantitatives à valeurs réelles ou intervalles, ordinales et/ou prenant la forme de distribution de probabilités/fréquences sur un(More)