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Sobre

Sobre

Sou Professora Associada na Faculdade de Economia da Universidade do Porto, onde ensino Estatística e Análise Multivariada de Dados a nível de licenciatura, mestrado e doutoramento, e membro do Laboratório em Inteligência Artificial e Sistemas de Apoio à Decisão (LIAAD) do INESC-TEC. Tenho um doutoramento em Matemática Aplicada da Universidade Paris Dauphine (1991).

A minha investigação actual centra-se na análise de dados multidimensionais complexos, usualmente designados por dados simbólicos - dados representado variabilidade inerente aos registos, sob a forma de intervalos ou distribuições - para os quais desenvolvo abordagens estatísticas e metodologias de análise multivariada.  De uma forma geral, interesso-me por análise multivariada de dados, com foco na análise classificatória.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Paula Brito
  • Cargo

    Investigador Coordenador
  • Desde

    01 janeiro 2008
001
Publicações

2023

Wavelet-based fuzzy clustering of interval time series

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

Publicação
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

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

Publicação
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

Classification and Data Science in the Digital Age

Autores
Brito, P; Dias, JG; Lausen, B; Montanari, A; Nugent, R;

Publicação
Studies in Classification, Data Analysis, and Knowledge Organization

Abstract

2023

Preface

Autores
Brito, P; Dias, G; Lausen, B; Montanari, A; Nugent, R;

Publicação
Studies in Classification, Data Analysis, and Knowledge Organization

Abstract
[No abstract available]

2023

Community detection in interval-weighted networks

Autores
Alves, H; Brito, P; Campos, P;

Publicação
DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
In this paper we introduce and develop the concept of interval-weighted networks (IWN), a novel approach in Social Network Analysis, where the edge weights are represented by closed intervals composed with precise information, comprehending intrinsic variability. We extend IWN for both Newman's modularity and modularity gain and the Louvain algorithm, considering a tabular representation of networks by contingency tables. We apply our methodology to two real-world IWN. The first is a commuter network in mainland Portugal, between the twenty three NUTS 3 Regions (IWCN). The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015 (IWTN). The optimal partition of geographic locations (regions or countries) is developed and compared using two new different approaches, designated as Classic Louvain and Hybrid Louvain , which allow taking into account the variability observed in the original network, thereby minimizing the loss of information present in the raw data. Our findings suggest the division of the twenty three Portuguese regions in three main communities for the IWCN and between two to three country communities for the IWTN. However, we find different geographical partitions according to the community detection methodology used. This analysis can be useful in many real-world applications, since it takes into account that the weights may vary within the ranges, rather than being constant.

Teses
supervisionadas

2022

Searching for Symbolic Patterns in Attributed Networks

Autor
Maria Hermínia Esteves de Carvalho

Instituição
UP-FCUP

2022

Multi-class Classification for Distributional Data

Autor
Ana Carolina Rodrigues dos Santos

Instituição
UP-FCUP

2022

Functional density symbolic data analysis

Autor
Rui Miguel da Cunha Nunes

Instituição
UP-FCUP

2021

Symbolic and Compositional Analysis of Textual Data

Autor
Tânia Manuela Costa da Silva

Instituição
UP-FEP

2020

Interval-Weighted Networks: Community Detection and Centrality Measures

Autor
Hélder Fernando Cerqueira Alves

Instituição
UP-FCUP