Inbound tourists segmentation with combined algorithms using K-Means and Decision Tree

Tourism is one of the main industries which bring about monetary to its country. To survive in the competitive industries these tourism organizations must have innovative strategies to carry on their business. One of the tools is tourism market segmentation which is used for strategic planning. This study presents inbound tourist market segmentation with combined algorithms using K-Means and Decision Tree. The study was divided into two phases. In the clustering phase, the segmentation was performed by Self Organizing Map (SOM) and K-Means. SOM used for determining the appropriate number of cluster. Then, K-Means used for refined the tourist clusters. The results of clustering phase were analyzed. In the classification phase, three classifiers were compared the performances of predictability by using the output provided by K-Means, i.e. Decision Tree, NaYve Bayes and Multilayer Perceptron (MLP). The experimental results indicated that SOM provided 6 clusters and K-Means gave better performance than SOM guided by Silhouette, Root Means Square Standard Deviation (RMSSTD) and R Square (RS). The predictive ability of J48 Decision Tree outperformed both of MLP and NaYve Bayes based on the tourist variables. J48 Decision Tree indicated the accuracy as 99.54%. The results of this study can be used for tourism management products and services.