Constrained Sequential Pattern Knowledge in Multi-relational Learning

  • Carlos Abreu Ferreira
  • João Gama
  • Vítor Santos Costa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7026)


In this work we present XMuSer, a multi-relational framework suitable to explore temporal patterns available in multi-relational databases. XMuSer’s main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequential miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation.

We evaluate our framework by addressing three classification problems. Moreover, we map each one of three different types of sequential patterns: frequent sequences, closed sequences or maximal sequences.


Sequential Pattern Inductive Logic Programming Frequent Sequence Fact Table Mining Sequential Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carlos Abreu Ferreira
    • 1
  • João Gama
    • 2
  • Vítor Santos Costa
    • 3
  1. 1.LIAAD-INESC and ISEPPolytechnic Institute of PortoPortoPortugal
  2. 2.LIAAD-INESC and Faculty of EconomicsUniversity of PortoPortoPortugal
  3. 3.CRACS-INESC and Faculty of SciencesUniversity of PortoPortoPortugal

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