Development of Domestic Tourists Model Using Clustering and Association Rule Techniques Case Study: Phra Nakhon Si Ayutthaya Province
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Abstract
The understanding of tourist needs is very complicated due to the extremely high number of tourists. Accordingly, providers cannot tailor their products or services to better match customers' needs and demands in each segment. This research proposes the model of domestic tourism in Phra Nakhon Si Ayutthaya province, Thailand by using machine learning techniques. The principle aims of this research are to study the behaviors of related domestic tourists and to develop a clustering model using the combination of clustering and association rule techniques. A data set was collected from 704 travelers who visited Phra Nakhon Si Ayutthaya. It was cleaned and prepared for clustering by the TwoStep cluster analysis. Then, the relevant association rules were discovered on each of these clusters. The experimental results revealed that the dataset could be divided into 4 different clusters, including senior tourists coming with a company trip (15.63%); elderly tourists traveling with family (37.78%), employee tourists (23.58%) and those who do enjoy traveling (23.01%). Each cluster showed as many as 8, 5, 3 and 6 association rules among their attributes with more than 80% confident. With insight into tourist travel behaviors, those involved can get engaged in strategic planning of products and services that are tailored to meet the specific needs of each cluster.
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