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Instituto Superior Técnico

Contacts

Name: Cláudia Antunes
E-mail: claudia dot antunes at tecnico dot ulisboa dot pt
Google Scholar: http://scholar.google.com/citations?hl=en&user=yQPwt38AAAAJ
Microsoft Academic Search: http://academic.research.microsoft.com/Author/23648308/claudia-antunes
DBLP: http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/a/Antunes:Cl=aacute=udia.html

Academic Degrees

  • PhD in Computers and Software Engineering, at Instituto Superior Técnico, Technical University of Lisbon, January 2005.
  • Masters Degree in Computers and Software Engineering, at Instituto Superior Técnico, Technical University of Lisbon, April 2001.
  • Graduation in Computers and Software Engineering, at Instituto Superior Técnico, Technical University of Lisbon, September 1998.

Scientific Activity

Area
  • Data science - Knowledge Discovery and Data Mining
  • Artificial Intelligence and Intelligent Systems
Domain of specialization
  • Sequential pattern mining
  • Temporal data analysis

  • Cláudia Antunes has concluded her PhD in the domain of data mining and machine learning, proposing new methods and methodologies for dealing with temporal data, in particular for mining event sequential patterns. In her thesis, she argued that “It is possible to efficiently use constrained sequential pattern mining algorithms over nominal temporal data to discover unknown information, keeping the process centered on the user.” For proving her claim, she introduced the concept of constraint relaxation and proposed methods for mining frequent patterns in the presence of domain knowledge, represented as a context-free language.
Main research interests
  • Domain driven data mining (D3M)
  • Semantic aspects of data mining
  • Mining complex knowledge from complex data
  • Structured pattern mining
  • Temporal data mining

  • Cláudia has been working on algorithms and methodologies for pattern mining, from transactional to structured data. Her main interests are centered on mining complex knowledge from complex data, with emphasis on the incorporation of background knowledge in the data mining process. Her work has been around the usage of domain ontologies to represent that knowledge, and she has been working on methods to efficiently mine patterns, guided by that knowledge.
    From the experience on pattern mining with background knowledge, her research has transitioned to approach the classification task, either by extending traditional training algorithms or by enriching the learning process to make use of the discovered patterns.
    Time is a particular dimension that has deserved considerable attention along years. However, the obtainable models continue to present poor results on problems where time is essential, like for example prognosis problems. Along years, her research has addressed this issue.
    Cláudia has been applying and validating these methods on areas from healthcare to education, also passing through the analysis of urban and forest data.

Research Projects

2011-2014 D2PM Coordinator FCT funding - PTDC/EIA-EIA/110074/2009
2011-2014 educare Coordinator FCT funding - PTDC/EIA-EIA/110058/2009
2010-2012 EuDML Researcher European Commission funding - CIP-ICT-PSP.2009.2.4
2008-2009 TELplus Researcher European Commission funding - ECP-2006-DILI-510003