Volume 9 Number 5 (May 2014)
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JSW 2014 Vol.9(5): 1334-1341 ISSN: 1796-217X
doi: 10.4304/jsw.9.5.1334-1341

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

Anongnart Srivihok, Wirot Yotsawat
Department of Computer Science, Faculty of Science Kasetsart University, Bangkok, Thailand

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), KMeans 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 Naïve 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 Naïve 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.

Index Terms—market segmentation, tourism, K-Means, unsupervised learning, supervise learning, Naïve Bayes

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Cite: Anongnart Srivihok, Wirot Yotsawat, "Market Segmentation of Inbound Business Tourists to Thailand by Binding of Unsupervised and Supervised Learning Techniques," Journal of Software vol. 9, no. 5, pp. 1334-1341, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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