Analyzing Tourist Activity Patterns Using Association Rule Mining: A Case Study of Chiang Rai Night Market
DOI:
https://doi.org/10.53848/jlsco.v12i2.298370Keywords:
Association Rule Mining (ARM), Tourist Behavior Patterns, FP-Growth Algorithm, Night Market Spatial PlanningAbstract
This research aims to explore patterns of tourist activity participation and analyze the relationships between tourist activities at Chiang Rai night market in order to support effective urban tourism management. A descriptive quantitative research design was employed. Data were collected from 405 tourists visiting the Chiang Rai Night Market using a structured questionnaire covering demographic characteristics, travel behavior, and activity participation. Descriptive statistical analysis, including frequency distribution and percentages, was used to examine tourist characteristics and overall participation. In addition, activity relationship patterns were analyzed using association rule mining with the FP-Growth algorithm, utilizing minimum thresholds of 0.60 for support value and 0.95 for confidence value, implemented through RapidMiner. The results showed that tourist activities were highly interconnected, creating integrated experiences. Tourists who participate in sightseeing and cultural learning activities show a strong tendency to engage in food consumption activities. Moreover, food consumption appears to be the central activity in most activity patterns at the market. The most frequent pattern involves dining combined with watching local music performances, which subsequently leads to sightseeing experiences within the night market environment. The practical implications suggest that the spatial planning of night markets should position food-related activities as the core attraction, while integrating cultural performances and experiential activities along major circulation areas, and allocating infrastructure in alignment with identified activity clusters to enhance tourist experiences and support sustainable urban tourism management, contributing new evidence on activity-level behavioral patterns in informal tourism settings where such data-driven analysis remains limited.
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