Publications

Publications by LIAAD

2023

Discovery Science

Authors
Bifet, A; Lorena, AC; Ribeiro, RP; Gama, J; Abreu, PH;

Publication
Lecture Notes in Computer Science

Abstract

2023

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Authors
Koprinska, I; Mignone, P; Guidotti, R; Jaroszewicz, S; Fröning, H; Gullo, F; Ferreira, PM; Roqueiro, D; Ceddia, G; Nowaczyk, S; Gama, J; Ribeiro, R; Gavaldà, R; Masciari, E; Ras, Z; Ritacco, E; Naretto, F; Theissler, A; Biecek, P; Verbeke, W; Schiele, G; Pernkopf, F; Blott, M; Bordino, I; Danesi, IL; Ponti, G; Severini, L; Appice, A; Andresini, G; Medeiros, I; Graça, G; Cooper, L; Ghazaleh, N; Richiardi, J; Saldana, D; Sechidis, K; Canakoglu, A; Pido, S; Pinoli, P; Bifet, A; Pashami, S;

Publication
Communications in Computer and Information Science

Abstract

2023

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Publication
Communications in Computer and Information Science

Abstract

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.

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2023

Classification and Data Science in the Digital Age

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

Publication
Studies in Classification, Data Analysis, and Knowledge Organization

Abstract

2023

Preface

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

Publication
Studies in Classification, Data Analysis, and Knowledge Organization

Abstract
[No abstract available]

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