Temporal Data Mining with Temporal Constraints

  • M. Campos
  • J. Palma
  • R. Marín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4594)


Nowadays, methods for discovering temporal knowledge try to extract more complete and representative patterns. The use of qualitative temporal constraints can be helpful in that aim, but its use should also involve methods for reasoning with them (instead of using them just as a high level representation) when a pattern consists of a constraint network instead of an isolated constraint.

In this paper, we put forward a method for mining temporal patterns that makes use of a formal model for representing and reasoning with qualitative temporal constraints. Three steps should be accomplished in the method: 1) the selection of a model that allows a trade off between efficiency and representation; 2) a preprocessing step for adapting the input to the model; 3) a data mining algorithm able to deal with the properties provided by the model for generating a representative output.

In order to implement this method we propose the use of the Fuzzy Temporal Constraint Network (FTCN) formalism and of a temporal abstraction method for preprocessing. Finally, the ideas of the classic methods for data mining inspire an algorithm that can generate FTCNs as output.

Along this paper, we focus our attention on the data mining algorithm.


Data Mining Temporal Pattern Temporal Relation Constraint Propagation Temporal Constraint 
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 2007

Authors and Affiliations

  • M. Campos
    • 1
  • J. Palma
    • 2
  • R. Marín
    • 2
  1. 1.Informatics and Systems Dept. Computer Science Faculty. University of Murcia 
  2. 2.Information and Communications Engineering Dept. Computer Science Faculty. University of Murcia 

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