An Iterative Method for Mining Frequent Temporal Patterns

  • Francisco Guil
  • Antonio Bailón
  • Alfonso Bosch
  • Roque Marín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3643)


The incorporation of temporal semantic into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. However, this process is computationally complex, and therefore it poses more challenges on efficient processing that non-temporal techniques. Based in the inter-transactional framework, in [11] we proposed an algorithm named TSET for mining temporal patterns (sequences) from datasets which uses a unique tree-based structure for storing all frequent patterns discovered in the mining process. However, in each data mining process, the algorithm must generate the whole structure from scratch. In this work, we propose an extension which consists in the reusing of structures generated in previous data mining process in order to reduce the execution time of the algorithm.


Data Mining Association Rule Mining Association Rule Fuzzy Association Rule Data Mining Process 
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 2005

Authors and Affiliations

  • Francisco Guil
    • 1
  • Antonio Bailón
    • 2
  • Alfonso Bosch
    • 1
  • Roque Marín
    • 3
  1. 1.Departamento de Lenguajes y ComputaciónUniversidad de AlmeríaAlmería
  2. 2.Dept. Ciencias de la Computación e Inteligencia ArtificialUniversidad de GranadaGranada
  3. 3.Dept. Ingeniería de la Información y las ComunicacionesUniversidad de MurciaEspinardo (Murcia)

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