DOI: 10.5176/978-981-08-7466-7_kd-23
Authors: Polkrit Chutipong and Supot Nitsuwat
Abstract:
The tourism service sector is highly significant for acountry’s economy. Many tourism researches have been studied and developed to suggest attractive places but when concerning seasonality and services, it seemed to be incomplete. This paper proposes a new decision support system model to serve in the tourism service industry by focusing on both factors. Moreover, the algorithm model management was enhanced by applying mining techniques to recommend tourism places. Naive Bayestree (NBTree) classifier is recommended by this study with acomparison to the basic Decision Tree algorithm. After implemented and evaluated, it seemed NBTree induction was better than the normal tree in two aspects. Firstly, better suggestion by providing advice using NB rules. Secondly, more accurate as evaluated by 10-fold cross validation. Importantly,Tourism knowledge can be interpreted for service providers using mining inductions as well.
Keywords: component; Tourism, Decision Support System,Recommendation system, Naïve Bayes Tree, Decision Tree
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