COPPER - Constraint optimized prefixspan for epidemiological research

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

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


Sequential pattern mining, is a data mining technique used to study the temporal evolution of events describing a complex phenomenon. 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.

Original languageEnglish
Pages (from-to)433-438
Number of pages6
JournalProcedia Computer Science
StatePublished - 2015
Event6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2015 - Berlin, Germany
Duration: 27 Sep 201530 Sep 2015


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


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