Market Segmentation of Inbound Business Tourists to Thailand by Binding of Unsupervised and Supervised Learning Techniques

Abstract

Market segmentation is an important tool, for driving an organization to achieve its goals. This study proposes a market segmentation technique with the binding of unsupervised and supervised learning techniques. The method aims to cluster international tourists who arrived in Thailand for business proposes, and to classify business tourists by using the products of an unsupervised learning technique as class labels. A Self-Organizing Map (SOM), K- Means and Hierarchical clustering were applied to find the best quality of segmentation guided by the computation of the Silhouette index. Segment labels were used to supervise the learning part as class labels. Multilayer Perceptron (MLP), J48 decision tree, Decision Table, OneR and Naive Bayes classifiers were used to classify the business tourist data set, and the best performance technique was preferred. The experimental results designated that K-Means outperformed the other clustering techniques and provided five different segments. Moreover, the Naive Bayes classifier gave the best performance among the other classifiers based on the business tourist variables. Thus, this model can be used to predict the segment of new arrival business tourists.