2014
DOI: 10.1016/j.tourman.2014.02.009
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Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium

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Cited by 112 publications
(57 citation statements)
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“…However, the proposed travel diaries are large scale and contain more contextual information about tourist activities than pure GPS information in previous studies (Versichele et al 2014;Vu et al 2015;Chua et al 2016, Vu et al 2017. Inferring tourist activities based on venue check-ins is more convenient than depending on other contents, such as photos, and textual comments.…”
Section: Discussionmentioning
confidence: 99%
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“…However, the proposed travel diaries are large scale and contain more contextual information about tourist activities than pure GPS information in previous studies (Versichele et al 2014;Vu et al 2015;Chua et al 2016, Vu et al 2017. Inferring tourist activities based on venue check-ins is more convenient than depending on other contents, such as photos, and textual comments.…”
Section: Discussionmentioning
confidence: 99%
“…The subjective perspectives of tourists, which were reflected in the venue tips, were not reported because of length limitation. The proposed travel diaries with check-in data can be adopted into the future analysis using data mining techniques, such as Association Rules Mining (Versichele et al 2014), Markov Chains (Vu et al 2015), and Sequential Rules Mining (Vu et al 2017), to reveal additional insights into the activity patterns of tourists.…”
Section: Discussionmentioning
confidence: 99%
“…The technical details of the association rule learning method are available in Versichele et al [12]. The quantitative analysis was concerned with the relationship between visitor satisfaction and the expression of local experiences in the review.…”
Section: Analysesmentioning
confidence: 99%
“…As stated by Versichele et al [12], a 0.05 threshold level was used for both the support and confidence. Figure 1 shows the results where the locals were linked most closely with the terms lost, interesting, busy, and local.…”
Section: Text Analysismentioning
confidence: 99%
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