An Apriori-based Approach for First-Order Temporal Pattern


  • Sandra de Amo Universidade Federal de Uberlândia
  • Daniel A. Furtado Universidade Federal de Uberlândia
  • Arnaud Giacometti Université de Tours
  • Dominique Laurent ETIS-CNRS-ENSEA-Université de Cergy Pontoise


Previous studies on mining sequential patterns have focused on temporal patterns specified by some form of
propositional temporal logic. However, there are some interesting sequential patterns whose specification needs a more expressive formalism, the first-order temporal logic. In this article, we focus on the problem of mining multi-sequential patterns which are first-order temporal patterns (not expressible in propositional temporal logic). We propose two Apriori-based algorithms to perform this mining task. The first one, the PM (Projection Miner) Algorithm adapts
the key idea of the classical GSP algorithm for propositional sequential pattern mining by projecting the first-order
pattern in two propositional components during the candidate generation and pruning phases. The second algorithm,
the SM (Simultaneous Miner) Algorithm, executes the candidate generation and pruning phases without decomposing the pattern, that is, the mining process, in some extent, does not reduce itself to its propositional counterpart. Our extensive experiments shows that SM scales up far better than PM.


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Author Biographies

Sandra de Amo, Universidade Federal de Uberlândia

Faculdade de Computação

Associate Professor

Daniel A. Furtado, Universidade Federal de Uberlândia

Faculdade de Engenharia Elétrica

PhD Student

Arnaud Giacometti, Université de Tours

LI-Université de Tours, UFR de Sciences


Dominique Laurent, ETIS-CNRS-ENSEA-Université de Cergy Pontoise

Université de Cergy Pontoise