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Tourist’s Tour Prediction by Sequential Data Mining Approach

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11888)


This paper answers the problem of predicting future behaviour tourist based on past behaviour of an individual tourist. The individual behaviour is naturally an indicator of the behaviour of other tourists. The prediction of tourists movement has a crucial role in tourism marketing to create demand and assist tourists in decision-making. With advances in information and communication technology, social media platforms generate data from millions of people from different countries during their travel. The main objective of this paper is to consider sequential data-mining methods to predict tourist movement based on Instagram data. Rules emerge from those ones are exploited to predict future behaviors. The originality of this approach is a combination between pattern mining to reduce the size of data and the automata to condense the rules. The capital city of France, Paris is selected to demonstrate the utility of the proposed methodology.


  • Sequential pattern mining
  • Sequential rule mining
  • Sequence prediction
  • Big data
  • Social network
  • Tourism

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Correspondence to Lilia Ben Baccar , Sonia Djebali or Guillaume Guérard .

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Ben Baccar, L., Djebali, S., Guérard, G. (2019). Tourist’s Tour Prediction by Sequential Data Mining Approach. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham.

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  • Print ISBN: 978-3-030-35230-1

  • Online ISBN: 978-3-030-35231-8

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