COPPER - Constraint optimized prefixspan for epidemiological research

Agustin Guevara-Cogorno, Claude Flamand, Hugo Alatrista-Salas

Producción científica: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

6 Citas (Scopus)


Sequential pattern mining, is a data mining technique used to study the temporal evolution of events describing a complex phe- nomenon. This technique has a limited application due to the high number of common sequences generated by dense datasets. To tackle this problem, we propose COP, an extension of the PrefixSpan algorithm oriented towards optimizing the relevance of the results obtained in the sequential patterns mining process. Indeed, we use multiple and simultaneous constraints that represent the expertise of researchers in a specific domain. Experiments conducted on datasets associated to dengue epidemic monitoring show an improve in result relevance from an expert's point of view, as well as, a considerable speed gains for mining dense datasets.
Idioma originalInglés
Páginas (desde-hasta)433-438
Número de páginas6
PublicaciónProcedia Computer Science
EstadoPublicada - 1 ene. 2015
Publicado de forma externa
Evento5th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, 2015 -
Duración: 1 ene. 20151 ene. 2015

Palabras clave

  • Constraints
  • Epidemiological databases
  • Healthcare risk management
  • Sequential patterns mining


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