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Clustering Method for Touristic Photographic Spots Recommendation

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 13726)


Tourism and photography have become very complementary, and tourists are constantly seeking the best spots to capture pictures and memorize their vacations. However, the search for the best and unforgettable photographic spots is difficult and time-consuming for tourists, especially when visiting new regions. In this paper, we propose a method for discovering tourist photo spots from geotagged photos using clustering algorithms. The clusters are characterized to determine the type of photos such as selfies or panoramic. We compare our approach to the most used clustering algorithms namely K-Means and DBSCAN. The approach is simulated and experimentally evaluated on a real photographic dataset of the French capital Paris. Our approach identifies the best-known, quirky and thematic spots in the reference websites.


  • Tourism
  • Photographic spots
  • Clustering
  • Knowledge discovery

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Correspondence to Sonia Djebali .

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Deseure-Charron, F., Djebali, S., Guérard, G. (2022). Clustering Method for Touristic Photographic Spots Recommendation. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13726. Springer, Cham.

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  • Print ISBN: 978-3-031-22136-1

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