Abstrait

Comparison of Supervised and Unsupervised Learning Classifiers for Travel Recommendations

Zohreh Bahman Isfahani, Shahram Jafari and Reza Akbarian

Classification is one of the most common activities in the related fields of intelligent decision making. Neural networks are suitable approaches for solving data mining problems especially classification. Usually for solving classification problem using neural network that proper outputs are existing for them, the supervised training type is selected. In this study a comparison about classification of electronic tourism data was done by using two learning method, supervised and unsupervised and the proper output values were determined for all the input data. The output includes some travel packages appropriate for the tourists recommended to them according to the input values. The experimental result showed that despite the target output values are exist ,the neural network output with unsupervised learning and SOM architecture has more precise prediction as compared to supervised learning .The neural network proposed travel packages is in more conformation with the tourist selections for final evaluation of the results the test dataset were given to the experts and their care in predictions indicated close results to the obtained evaluations from the unsupervised learning method.