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About

About

I am Associate Professor at the School of Economics of the University of Porto, where  I teach Statistics and Multivariate Data Analysis, at undergraduate and post-graduate (Master, PhD) levels, and member of the Artificial Intelligence and Decision Support Lab (LIAAD) of INESC-TEC. I hold a doctorate degree in Applied Mathematics from the University of Paris Dauphine (1991).

My current research focuses on the analysis of multidimensional complex data, known as symbolic data - data representing inherent variability, in the form of intervals or distributions - for which I develop statistical approaches and multivariate analysis methodologies.  I am generally interested in multivariate data analysis, with particular incidence in clustering methods.

Interest
Topics
Details

Details

  • Name

    Paula Brito
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st January 2008
001
Publications

2023

Wavelet-based fuzzy clustering of interval time series

Authors
D'Urso, P; De Giovanni, L; Maharaj, EA; Brito, P; Teles, P;

Publication
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING

Abstract
We investigate the fuzzy clustering of interval time series using wavelet variances and covariances; in particular, we use a fuzzy c-medoids clustering algorithm. Traditional hierarchical and non-hierarchical clustering methods lead to the identification of mutually exclusive clusters whereas fuzzy clustering methods enable the identification of overlapping clusters, implying that one or more series could belong to more than one cluster simultaneously. An interval time series (ITS) which arises when interval-valued observa-tions are recorded over time is able to capture the variability of values within each interval at each time point. This is in contrast to single-point information available in a classical time series. Our main contribution is that by combining wavelet analysis, interval data analysis and fuzzy clustering, we are able to capture information which would otherwise have not been contemplated by the use of traditional crisp clustering methods on classical time series for which just a single value is recorded at each time point. Through simulation studies, we show that under some circumstances fuzzy c-medoids clustering performs better when applied to ITS than when it is applied to the corresponding traditional time series. Applications to exchange rates ITS and sea-level ITS show that the fuzzy clustering method reveals different and more meaningful results than when applied to associated single-point time series.

2023

Wavelet-based fuzzy clustering of interval time series

Authors
D'Urso, P; De Giovanni, L; Maharaj, EA; Brito, P; Teles, P;

Publication
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING

Abstract
We investigate the fuzzy clustering of interval time series using wavelet variances and covariances; in particular, we use a fuzzy c-medoids clustering algorithm. Traditional hierarchical and non-hierarchical clustering methods lead to the identification of mutually exclusive clusters whereas fuzzy clustering methods enable the identification of overlapping clusters, implying that one or more series could belong to more than one cluster simultaneously. An interval time series (ITS) which arises when interval-valued observa-tions are recorded over time is able to capture the variability of values within each interval at each time point. This is in contrast to single-point information available in a classical time series. Our main contribution is that by combining wavelet analysis, interval data analysis and fuzzy clustering, we are able to capture information which would otherwise have not been contemplated by the use of traditional crisp clustering methods on classical time series for which just a single value is recorded at each time point. Through simulation studies, we show that under some circumstances fuzzy c-medoids clustering performs better when applied to ITS than when it is applied to the corresponding traditional time series. Applications to exchange rates ITS and sea-level ITS show that the fuzzy clustering method reveals different and more meaningful results than when applied to associated single-point time series.

2022

Centrality measures in interval-weighted networks

Authors
Alves, H; Brito, P; Campos, P;

Publication
JOURNAL OF COMPLEX NETWORKS

Abstract
Centrality measures are used in network science to assess the centrality of vertices or the position they occupy in a network. There are a large number of centrality measures according to some criterion. However, the generalizations of the most well-known centrality measures for weighted networks, degree centrality, closeness centrality and betweenness centrality have solely assumed the edge weights to be constants. This article proposes a methodology to generalize degree, closeness and betweenness centralities taking into account the variability of edge weights in the form of closed intervals (interval-weighted networks, IWN). We apply our centrality measures approach to two real-world IWN. The first is a commuter network in mainland Portugal, between the 23 NUTS 3 Regions. The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015.

2022

Analysis of Distributional Data

Authors
Brito, P; Dias, S;

Publication

Abstract

2022

The Quantile Methods to Analyze Distributional Data

Authors
Ichino, M; Brito, P;

Publication
Analysis of Distributional Data

Abstract

Supervised
thesis

2022

Searching for Symbolic Patterns in Attributed Networks

Author
Maria Hermínia Esteves de Carvalho

Institution
UP-FCUP

2022

Multi-class Classification for Distributional Data

Author
Ana Carolina Rodrigues dos Santos

Institution
UP-FCUP

2022

Functional density symbolic data analysis

Author
Rui Miguel da Cunha Nunes

Institution
UP-FCUP

2021

Symbolic and Compositional Analysis of Textual Data

Author
Tânia Manuela Costa da Silva

Institution
UP-FEP

2020

Interval-Weighted Networks: Community Detection and Centrality Measures

Author
Hélder Fernando Cerqueira Alves

Institution
UP-FCUP