Data Mining of International Tourists in Thailand by Two Step Clustering and Classification

Yotsawat, Wirot; Srivihok, Anongnart

doi:10.1166/asl.2014.5299

Cited by 1 publication

(2 citation statements)

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“…Past research focused on inbound tourist market segmentation [1] and proposed data mining of tourists by using two step clustering and classification. The research found that the K-Means technique gave higher quality information than SOM and Fuzzy C-Means (FCM) for tourist partitioning based on international tourists to Thailand.…”

Section: Introductionmentioning

confidence: 99%

“…The primary objectives of this study are (1) to compare the performances of K-Means, SOM neural network and Hierarchical clustering techniques in order to segment business tourists and (2) to compare the performance of classifiers namely, Decision Tree, Decision Table, OneR, MLP and Naïve Bayes, in order to predict the segment of new business tourists as part of the production from the clustering technique.…”

Section: Introductionmentioning

confidence: 99%

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

Srivihok¹,

Yotsawat²

2014

JSW

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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 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%