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About

I am a Lecturer at the Department of Informatics at the University of Minho. I am also a researcher at HASLab/INESC TEC. My research interest focus mainly on machine learning and data mining. Occasionally, I participate in Bioinformatics research projects involving analysis of molecular dynamic simulations of protein folding/unfolding.

 I hold a PhD in Computing from Imperial College (University of London) where I did research in logic programming. I have been working on the development of association rules mining algorithms and novel patterns to capture distribution learning. I also have interest in social network analysis, graph mining, subgroup mining and motif discovery in time series.

Interest
Topics
Details

Details

  • Name

    Paulo Jorge Azevedo
  • Role

    Affiliated Researcher
  • Since

    01st November 2011
  • Nationality

    Portugal
  • Contacts

    +351253604440
    paulo.j.azevedo@inesctec.pt
004
Publications

2018

Preference rules for label ranking: Mining patterns in multi-target relations

Authors
de Sa, CR; Azevedo, P; Soares, C; Jorge, AM; Knobbe, A;

Publication
INFORMATION FUSION

Abstract
In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

2018

Discovering a taste for the unusual: exceptional models for preference mining

Authors
de Sa, CR; Duivesteijn, W; Azevedo, P; Jorge, AM; Soares, C; Knobbe, A;

Publication
Machine Learning

Abstract
Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge. © 2018 The Author(s)

2015

Automatically estimating iSAX parameters

Authors
Castro, NC; Azevedo, PJ;

Publication
INTELLIGENT DATA ANALYSIS

Abstract
The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient. However, it requires setting two parameters: the symbolic length and alphabet size, which limits the applicability of the technique. The optimal parameter values are highly application dependent. Typically, they are either set to a fixed value or experimentally probed for the best configuration. In this work we propose an approach to automatically estimate iSAX's parameters. The approach - AutoiSAX - not only discovers the best parameter setting for each time series in the database, but also finds the alphabet size for each iSAX symbol within the same word. It is based on simple and intuitive ideas from time series complexity and statistics. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyze its impact in visualization interpretability, classification accuracy and motif mining. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method.

2015

Contrast set mining in temporal databases

Authors
Magalhães, A; Azevedo, PJ;

Publication
Expert Systems

Abstract

2015

Contrast set mining in temporal databases

Authors
Magalhaes, A; Azevedo, PJ;

Publication
EXPERT SYSTEMS

Abstract
Understanding the underlying differences between groups or classes in certain contexts can be of the utmost importance. Contrast set mining relies on discovering significant patterns by contrasting two or more groups. A contrast set is a conjunction of attribute-value pairs that differ meaningfully in its distribution across groups. A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task. We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line. To evaluate the proposal accuracy and ability to discover relevant information, two different real-life data sets were studied using this approach.

Supervised
thesis

2019

Active Learning and Intelligent Queues

Author
Miguel Lobo Pinto Leite

Institution
UM