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Identification

Personal identification

Full name
PAULO JORGE DE SOUSA AZEVEDO

Citation names

  • AZEVEDO, PAULO
  • Paulo J. Azevedo

Author identifiers

Ciência ID
EB1D-CA33-4DA3
ORCID iD
0000-0002-0877-3070

Websites

Outputs

Publications

Book chapter
  1. Carlos Soares; Paulo J. Azevedo; Vitor Cerqueira; Luis Torgo. "Meta Subspace Analysis: Understanding Model (Mis)behavior in the Metafeature Space". 2025.
    10.1007/978-3-032-05461-6_17
  2. Ferreira, PG; Azevedo, PJ. "Deterministic pattern mining on genetic sequences". 2009.
    10.4018/978-1-60566-766-9.ch013
  3. Silva, CG; Ferreira, PG; Azevedo, PJ; Brito, RMM. "Using data mining techniques to probe the role of hydrophobic residues in protein folding and unfolding simulations". 2009.
    10.4018/978-1-60566-816-1.ch012
  4. Ferreira, PG; Azevedo, PJ. "Deterministic Motif Mining in Protein Databases". 2009.
  5. Ferreira, PG; Azevedo, PJ. "Deterministic motif mining in protein databases". 2007.
    10.4018/978-1-59904-645-7.ch006
Conference paper
  1. Gomes, EF; Jorge, AM; Azevedo, PJ. "Classifying heart sounds using SAX motifs, random forests and text mining techniques". 2014.
    10.1145/2628194.2628240
  2. de Sa, CR; Soares, C; Knobbe, A; Azevedo, P; Jorge, AM. "Multi-interval Discretization of Continuous Attributes for Label Ranking". 2013.
    10.1007/978-3-642-40897-7_11
  3. Ferreira, PG; Azevedo, PJ. "Evaluating protein motif significance measures: A case study on prosite patterns". 2007.
    10.1109/cidm.2007.368869
  4. Ferreira, PG; Azevedo, PJ. "Query Driven Sequence Pattern Mining". 2006.
  5. Camacho, R; Alves, A; da Costa, JP; Azevedo, P. "CMB'05: Workshop on Computational Methods in Bioinformatics". 2005.
    10.1109/epia.2005.341279
  6. Ferreira, PG; Alves, R; Azevedo, PJ; Belo, O. "A Hybrid Method for Discovering Distance-Enhanced Inter-Transactional Rules". 2005.
  7. Azevedo, PJdS; Montesi, D. "An Extended Magic Sets Strategy for a Rule Language with Updates and Transactions". 1995.
  8. Azevedo, PJdS; Sergot, MJ. "Recomputation-Free Lemmatization by Program Transformation". 1994.
Journal article
  1. Tabassum, Shazia; Gama, Joao; Azevedo, Paulo J.; Cordeiro, Mario; Martins, Carlos; Martins, Andre. "Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs". (2023): https://hdl.handle.net/1822/90261.
    10.1111/exsy.13195
  2. Pimentel, Joao; Azevedo, Paulo J.; Torgo, Luis. "Subgroup mining for performance analysis of regression models". (2023): https://hdl.handle.net/1822/90257.
    10.1111/exsy.13118
  3. de Sa, CR; Duivesteijn, W; Azevedo, P; Jorge, AM; Soares, C; Knobbe, A. "Discovering a taste for the unusual: exceptional models for preference mining". MACHINE LEARNING (2018):
    10.1007/s10994-018-5743-z
  4. AZEVEDO, PAULO. "Preference rules for label ranking: Mining patterns in multi-target relations". Information Fusion (2018): https://doi.org/10.1016/j.inffus.2017.07.001.
    10.1016/j.inffus.2017.07.001
  5. de Sá, Cláudio Rebelo; Azevedo, Paulo J.; Soares, Carlos; Jorge, Alípio Mário; Knobbe, Arno. "Preference rules for label ranking: Mining patterns in multi-target relations". (2018): https://hdl.handle.net/1822/71614.
    10.1016/j.inffus.2017.07.001
  6. de Sa, Claudio Rebelo; Duivesteijn, Wouter; Azevedo, Paulo J.; Jorge, Alipio Mario; Soares, Carlos; Knobbe, Arno. "Discovering a taste for the unusual: exceptional models for preference mining". (2018): https://hdl.handle.net/1822/71611.
    10.1007/s10994-018-5743-z
  7. AZEVEDO, PAULO. "Automatically estimating iSAX parameters". Intell. Data Anal. 19 3 (2015): 581-595. https://doi.org/10.3233/IDA-150733.
    10.3233/IDA-150733
  8. Castro, NC; Azevedo, PJ. "Automatically estimating iSAX parameters". INTELLIGENT DATA ANALYSIS (2015):
    10.3233/ida-150733
  9. AZEVEDO, PAULO. "Contrast set mining in temporal databases". Expert Systems (2015): https://doi.org/10.1111/exsy.12080.
    10.1111/exsy.12080
  10. Castro, Nuno Constantino; Azevedo, Paulo J.. "Automatically estimating iSAX parameters". (2015): https://hdl.handle.net/1822/40542.
    10.3233/ida-150733
  11. Magalhães, André; Azevedo, Paulo J.. "Contrast set mining in temporal databases". (2015): https://hdl.handle.net/1822/33862.
    10.1111/exsy.12080
  12. Gomes, E.F.; Jorge, A.M.; Azevedo, P.J.. "Classifying heart sounds using multiresolution time series motifs: An exploratory study". ACM International Conference Proceeding Series (2013): 23-30. http://www.scopus.com/inward/record.url?eid=2-s2.0-84882771642&partnerID=MN8TOARS.
    10.1145/2494444.2494458
  13. Castro, N.C.; Azevedo, P.J.. "Significant motifs in time series". Statistical Analysis and Data Mining 5 1 (2012): 35-53. http://www.scopus.com/inward/record.url?eid=2-s2.0-84857169514&partnerID=MN8TOARS.
    10.1002/sam.11134
  14. Jorge, A.M.; Mendes-Moreira, J.; De Sousa, J.F.; Soares, C.; Azevedo, P.J.. "Finding interesting contexts for explaining deviations in bus trip duration using distribution rules". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7619 LNCS (2012): 139-149. http://www.scopus.com/inward/record.url?eid=2-s2.0-84868010720&partnerID=MN8TOARS.
    10.1007/978-3-642-34156-4_14
  15. Jorge, A.M.; Azevedo, P.J.. "Optimal leverage association rules with numerical interval conditions". Intelligent Data Analysis 16 1 (2012): 25-47. http://www.scopus.com/inward/record.url?eid=2-s2.0-84856860531&partnerID=MN8TOARS.
    10.3233/IDA-2011-0509
  16. Jorge, AM; Azevedo, PJ. "Optimal leverage association rules with numerical interval conditions". INTELLIGENT DATA ANALYSIS (2012):
    10.3233/ida-2011-0509
  17. Jorge, Alípio M.; Azevedo, Paulo J.. "Optimal leverage association rules with numerical interval conditions". (2012): https://hdl.handle.net/1822/33812.
    10.3233/IDA-2011-0509
  18. Castro, N.; Azevedo, P.J.. "Time series motifs statistical significance". Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011 (2011): 687-698. http://www.scopus.com/inward/record.url?eid=2-s2.0-84866018906&partnerID=MN8TOARS.
    10.1137/1.9781611972818.59
  19. De Sá, C.R.; Soares, C.; Jorge, A.M.; Azevedo, P.; Costa, J.. "Mining association rules for label ranking". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6635 LNAI PART 2 (2011): 432-443. http://www.scopus.com/inward/record.url?eid=2-s2.0-79957929921&partnerID=MN8TOARS.
    10.1007/978-3-642-20847-8-36
  20. Azevedo, P.J.. "Rules for contrast sets". Intelligent Data Analysis 14 6 (2010): 623-640. http://www.scopus.com/inward/record.url?eid=2-s2.0-78650361161&partnerID=MN8TOARS.
    10.3233/IDA-2010-0444
  21. Castro, N.; Azevedo, P.. "Multiresolution motif discovery in time series". Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010 (2010): 665-676. http://www.scopus.com/inward/record.url?eid=2-s2.0-80155203953&partnerID=MN8TOARS.
    10.1137/1.9781611972801.73
  22. Azevedo, P.J.; Jorge, A.M.. "Ensembles of jittered association rule classifiers". Data Mining and Knowledge Discovery 21 1 (2010): 91-129. http://www.scopus.com/inward/record.url?eid=2-s2.0-77953811058&partnerID=MN8TOARS.
    10.1007/s10618-010-0173-y
  23. Azevedo, PJ. "Rules for contrast sets". INTELLIGENT DATA ANALYSIS (2010):
    10.3233/ida-2010-0444
  24. Azevedo, Paulo J.. "Rules for contrast sets". (2010): https://hdl.handle.net/1822/34981.
    10.3233/IDA-2010-0444
  25. Azevedo, Paulo J.; Jorge, Alípio M.. "Ensembles of jittered association rule classifiers". (2010): https://hdl.handle.net/1822/33800.
    10.1007/s10618-010-0173-y
  26. Ferreira, P.G.; Silva, C.G.; Azevedo, P.J.; Brito, R.M.M.. "Spatial clustering of molecular dynamics trajectories in protein unfolding simulations". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5488 LNBI (2009): 156-166. http://www.scopus.com/inward/record.url?eid=2-s2.0-70349322630&partnerID=MN8TOARS.
    10.1007/978-3-642-02504-4_14
  27. Jorge, A.; Poças, J.; Azevedo, P.J.. "A methodology for exploring association models". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4404 LNCS (2008): 46-59. http://www.scopus.com/inward/record.url?eid=2-s2.0-50249171235&partnerID=MN8TOARS.
    10.1007/978-3-540-71080-6_4
  28. Ferreira, P.G.; Azevedo, P.J.. "Evaluating deterministic motif significance measures in protein databases". Algorithms for Molecular Biology 2 1 (2007): http://www.scopus.com/inward/record.url?eid=2-s2.0-44449158817&partnerID=MN8TOARS.
    10.1186/1748-7188-2-16
  29. Ferreira, P.G.; Silva, C.G.; Brito, R.M.M.; Azevedo, P.J.. "A closer look on protein unfolding simulations through hierarchical clustering". 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007 (2007): 461-468. http://www.scopus.com/inward/record.url?eid=2-s2.0-58149181357&partnerID=MN8TOARS.
  30. Azevedo, P.J.; Jorge, A.M.. "Comparing rule measures for predictive association rules". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4701 LNAI (2007): 510-517. http://www.scopus.com/inward/record.url?eid=2-s2.0-38049110563&partnerID=MN8TOARS.
    10.1007/978-3-540-74958-5_47
  31. Azevedo, P.J.; Jorge, A.M.. "Iterative reordering of rules for building ensembles without relearning". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4755 LNAI (2007): 56-67. http://www.scopus.com/inward/record.url?eid=2-s2.0-38149051571&partnerID=MN8TOARS.
    10.1007/978-3-540-75488-6_7
  32. Ferreira, P.G.; Azevedo, P.J.. "Evaluating protein motif significance measures: A case study on prosite patterns". Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 (2007): 171-178. http://www.scopus.com/inward/record.url?eid=2-s2.0-34548784572&partnerID=MN8TOARS.
    10.1109/CIDM.2007.368869
  33. Ferreira, P.G.; Azevedo, P.J.; Silva, C.G.; Brito, R.M.M.. "Mining approximate motifs in time series". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4265 LNAI (2006): 89-101. http://www.scopus.com/inward/record.url?eid=2-s2.0-33750736111&partnerID=MN8TOARS.
    10.1007/11893318_12
  34. Jorge, A.M.; Pereira, F.; Azevedo, P.J.. "Visual interactive subgroup discovery with numerical properties of interest". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4265 LNAI (2006): 301-305. http://www.scopus.com/inward/record.url?eid=2-s2.0-33750727738&partnerID=MN8TOARS.
    10.1007/11893318_31
  35. Jorge, A.M.; Azevedo, P.J.; Pereira, F.. "Distribution rules with numeric attributes of interest". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4213 LNAI (2006): 247-258. http://www.scopus.com/inward/record.url?eid=2-s2.0-33750339325&partnerID=MN8TOARS.
    10.1007/11871637_26
  36. Azevedo, Paulo J.; Ferreira, Pedro Gabriel. "Query driven sequence pattern mining". (2006): https://hdl.handle.net/1822/6588.
  37. Camacho, R; Alves, A; da Costa, JP; Azevedo, P. "12th Portuguese Conference on Artificial Intelligence, EPIA 2005 Covilha, Portugal, December 5-8, 2005 - Introduction". PROGRESS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS (2005):
  38. Azevedo, P.J.; Silva, C.G.; Rodrigues, J.R.; Loureiro-Ferreira, N.; Brito, R.M.M.. "Detection of hydrophobic clusters in molecular dynamics protein unfolding simulations using association rules". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3745 LNBI (2005): 329-337. http://www.scopus.com/inward/record.url?eid=2-s2.0-33745292575&partnerID=MN8TOARS.
    10.1007/11573067_33
  39. Jorge, A.M.; Azevedo, P.J.. "An experiment with association rules and classification: Post-bagging and conviction". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3735 LNAI (2005): 137-149. http://www.scopus.com/inward/record.url?eid=2-s2.0-33745317786&partnerID=MN8TOARS.
    10.1007/11563983_13
  40. Camacho, R.; Alves, A.; Da Costa, J.P.; Azevedo, P.. "Lecture Notes in Artificial Intelligence: Introduction". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3808 LNCS (2005): http://www.scopus.com/inward/record.url?eid=2-s2.0-33744818159&partnerID=MN8TOARS.
    10.1007/11595014_23
  41. Ferreira, P.G.; Azevedo, P.J.. "Protein sequence classification through relevant sequence mining and bayes classifiers". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3808 LNCS (2005): 236-244. http://www.scopus.com/inward/record.url?eid=2-s2.0-33744822458&partnerID=MN8TOARS.
    10.1007/11595014_24
  42. Ferreira, P.G.; Azevedo, P.J.. "Protein sequence pattern mining with constraints". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3721 LNAI (2005): 96-107. http://www.scopus.com/inward/record.url?eid=2-s2.0-33646410657&partnerID=MN8TOARS.
    10.1007/11564126_14
  43. Veloso, M.; Jorge, A.; Azevedo, P.J.. "Model-based collaborative filtering for team building support". ICEIS 2004 - Proceedings of the Sixth International Conference on Enterprise Information Systems (2004): 241-248. http://www.scopus.com/inward/record.url?eid=2-s2.0-8444225767&partnerID=MN8TOARS.
  44. Jorge, A; Pocas, J; Azevedo, P. "Post-processing operators for browsing large sets of association rules". DISCOVERY SCIENCE, PROCEEDINGS (2002):
    10.1007/3-540-36182-0_43
  45. Azevedo, P.J.. "Magic sets with full sharing". Journal of Logic Programming 30 3 (1997): 222-237. http://www.scopus.com/inward/record.url?eid=2-s2.0-0031102949&partnerID=MN8TOARS.
    10.1016/s0743-1066(96)00119-7
  46. Azevedo, Paulo J.. "Magic sets with full sharing". (1997): https://hdl.handle.net/1822/2216.
Report
  1. Azevedo, Paulo J.. 2003. CAREN - A java based apriori implementation for classification purposes. https://hdl.handle.net/1822/1982.
Thesis / Dissertation
  1. Andrade, Francisco Alves. "Azure machine-learning service and AI-Driven application for content management". Master, 2024. https://hdl.handle.net/1822/93198.
  2. Neto, Luís Paulo Ferreira Gomes. "Development of a bot like entity to emulate an user in a tridimensional virtual environment". Master, 2023. https://hdl.handle.net/1822/92593.
  3. Pimentel, João Pedro Torres. "Machine learning interpretability in a context of black box regression models". Master, 2021. https://hdl.handle.net/1822/81339.
  4. Leite, Miguel Lobo Pinto. "Active learning for fraud detection". Master, 2020. https://hdl.handle.net/1822/84134.
  5. Festa, Paulo Sérgio de Almeida. "Deteção e validação de comportamento desviante no combate à fraude em modelos de publicidade pay-per-click". Master, 2012. https://hdl.handle.net/1822/28285.
  6. Lopes, Eduardo Luís da Silva. "Deteção de anomalias em modelos de publicidade Pay-Per-Click". Master, 2012. https://hdl.handle.net/1822/28035.
  7. Magalhães, André Filipe Gonçalves. "Data Mining methods to detect discrimination patterns along temporal databases". Master, 2012. https://hdl.handle.net/1822/27995.
  8. Gregório, Carla Alexandra Marques. "Análise de dados de desnaturação proteica obtida por simulações de dinâmica molecular". Master, 2012. https://hdl.handle.net/1822/27882.
  9. Castro, Nuno Constantino. "Time series motif discovery". PhD, 2012. https://hdl.handle.net/1822/22943.
  10. Ferreira, Pedro Gabriel Dias. "Sequence pattern mining in biochemical data". PhD, 2007. https://hdl.handle.net/1822/7257.

Other

Other output
  1. Beyond Average Performance -- exploring regions of deviating performance for black box classification models. Machine learning models are becoming increasingly popular in different types of settings. This is mainly caused by their ability to achieve a level of predictive performance that is hard to match by human experts in this new era of big data. With this usage growth comes an increase of the requirements for accountability and understanding of the models' predictions. However, the degree of sophistic. 2021. Torgo, Luis; Azevedo, Paulo J.; Areosa, Inês. https://hdl.handle.net/1822/90265.
    10.48550/arXiv.2109.08216
  2. Dynamic topic modeling using social network analytics. Topic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as. 2021. Tabassum, Shazia; Gama, João; Azevedo, Paulo J.; Teixeira, Luis; Martins, Carlos; Martins, Andre. https://hdl.handle.net/1822/90304.
    10.1007/978-3-030-86230-5_39
  3. Sequence mining for automatic generation of software tests from GUI event traces. In today’s software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and effort. This paper presents a framework that generates software test cases automatically based on us. 2020. Oliveira, Alberto; Freitas, Ricardo; Jorge, Alípio; Amorim, Vítor; Moniz, Nuno; Paiva, Ana C.R.; Azevedo, Paulo J.. https://hdl.handle.net/1822/71380.
    10.1007/978-3-030-62365-4_49
  4. Preference rules for label ranking: Mining patterns in multi-target relations. 2019. de Sá, CR; Azevedo, PJ; Soares, C; Jorge, AM; Knobbe, AJ.
  5. Classifying heart sounds using SAX motifs, random forests and text mining techniques. In this paper we describe an approach to classifying heart sounds (classes Normal, Murmur and Extra-systole) that is based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The ability of automatically classifying heart sounds or at least support human decision in this task is socially relevant to spread the reach of medical care using simple m. 2014. Gomes, Elsa Ferreira; Jorge, Alípio M.; Azevedo, Paulo J.. https://hdl.handle.net/1822/33769.
    10.1145/2628194.2628240
  6. Multi-interval discretization of continuous attributes for label ranking. Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data di. 2013. Sá, Cláudio Rebelo de; Soares, Carlos; Knobbe, Arno; Azevedo, Paulo J.; Jorge, Alípio Mário. https://hdl.handle.net/1822/51323.
    10.1007/978-3-642-40897-7_11
  7. Classifying heart sounds using multiresolution time series motifs : an exploratory study. The aim of this work is to describe an exploratory study on the use of a SAX-based Multiresolution Motif Discovery method for Heart Sound Classification. The idea of our work is to discover relevant frequent motifs in the audio signals and use the discovered motifs and their frequency as characterizing attributes. We also describe different configurations of motif discovery for defining attributes. 2013. Gomes, Elsa Ferreira; Jorge, Alípio M.; Azevedo, Paulo J.. https://hdl.handle.net/1822/33768.
  8. Finding interesting contexts for explaining deviations in bus trip duration using distribution rules. In this paper we study the deviation of bus trip duration and its causes. Deviations are obtained by comparing scheduled times against actual trip duration and are either delays or early arrivals. We use distribution rules, a kind of association rules that may have continuous distributions on the consequent. Distribution rules allow the systematic identification of particular conditions, which we. 2012. Jorge, Alípio M.; Moreira, João Mendes; Sousa, Jorge Freire de; Soares, Carlos; Azevedo, Paulo J.. https://hdl.handle.net/1822/36005.
    10.1007/978-3-642-34156-4_14
  9. Time series motifs statistical significance. Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of eficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by. 2011. Castro, Nuno Constantino; Azevedo, Paulo J.. https://hdl.handle.net/1822/36012.
    10.1137/1.9781611972818.59
  10. Mining association rules for label ranking. Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm. Given that the original APRIORI algorithm does not aim to obtain predictive models, two changes were needed for this achievement.. 2011. Sá, Cláudio Rebelo; Soares, Carlos; Jorge, Alípio M.; Azevedo, Paulo J.; Costa, Joaquim. https://hdl.handle.net/1822/36011.
    10.1007/978-3-642-20847-8-36
  11. Multiresolution motif discovery in time series. Time series motif discovery is an important problem with applications in a variety of areas that range from telecommunications to medicine. Several algorithms have been proposed to solve the problem. However, these algorithms heavily use expensive random disk accesses or assume the data can't into main memory. They only consider motifs at a single resolution and are not suited to interactivity. In. 2010. Castro, Nuno Constantino; Azevedo, Paulo J.. https://hdl.handle.net/1822/36013.
    10.1137/1.9781611972801.73
  12. Spatial clustering of molecular dynamics trajectories in protein unfolding simulations. Molecular dynamics simulations is a valuable tool to study protein unfolding in silico. Analyzing the relative spatial position of the residues during the simulation may indicate which residues are essential in determining the protein structure. We present a method, inspired by a popular data mining technique called Frequent Itemset Mining, that clusters sets of amino acid residues with a synchron. 2009. Azevedo, Paulo J.; Ferreira, Pedro Gabriel; Silva, Cândida G.; Brito, Rui M. M.. https://hdl.handle.net/1822/37801.
    10.1007/978-3-642-02504-4_14
  13. Visual interactive subgroup discovery with numerical properties of interest. Subgroup discovery consists in finding subsets of individuals from a given population which have distinctive collective properties with regard to one or more properties of interest. The interest of a subgroup can be objectively assessed using appropriate statistics, but it can also be evaluated by a data analyst or domain expert. In this paper we propose an approach to subgroup discovery via distr. 2006. Jorge, Alípio M.; Pereira, Fernando; Azevedo, Paulo J.. https://hdl.handle.net/1822/6584.
    10.1007/11893318_31
  14. Mining approximate motifs in time series. The problem of discovering previously unknown frequent patterns in time series, also called motifs, has been recently introduced. A motif is a subseries pattern that appears a significant number of times. Results demonstrate that motifs may provide valuable insights about the data and have a wide range of applications in data mining tasks. The main motivation for this study was the need to mine ti. 2006. Azevedo, Paulo J.; Ferreira, Pedro Gabriel; Silva, Cândida G.; Brito, Rui M. M.. https://hdl.handle.net/1822/6422.
  15. Protein sequence pattern mining with constraints. Considering the characteristics of biological sequence databases, which typically have a small alphabet, a very long length and a relative small size (several hundreds of sequences), we propose a new sequence mining algorithm (gIL). gIL was developed for linear sequence pattern mining and results from the combination of some of the most efficient techniques used in sequence and itemset mining. The. 2005. Ferreira, Pedro Gabriel; Azevedo, Paulo J.. https://hdl.handle.net/1822/6293.
    10.1007/11564126_14
  16. An experiment with association rules and classification: post-bagging and conviction. In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy.. 2005. Jorge, Alípio M.; Azevedo, Paulo J.. https://hdl.handle.net/1822/4295.
    10.1007/11563983_13