Tourism knowledge discovery through data mining techniques

Abstract

Tourism industry in Malaysia has been customarily thought and advanced towards universal markets since its early stages arrange in 1960s. Currently, study about tourism knowledge discovery is very little being addressed. The previous studies are still insufficient to extract important insights from tourism data within Malaysia context. Therefore, this paper aims to analyze profiles of tourists using data mining decision tree techniques where several combinations of the number of branches (2 and 3 branches) and different target splitting rules (Entropy, Gini, and Probability Chi-square) have been applied on comprehensive survey data and to find out the best performing algorithm among the six models for tourism knowledge discovery. Results show that there are a various type of tourists with each group having different patterns or rules. This research study can be very helpful for tourist association, hospitality and hotel managers.Tourism industry in Malaysia has been customarily thought and advanced towards universal markets since its early stages arrange in 1960s. Currently, study about tourism knowledge discovery is very little being addressed. The previous studies are still insufficient to extract important insights from tourism data within Malaysia context. Therefore, this paper aims to analyze profiles of tourists using data mining decision tree techniques where several combinations of the number of branches (2 and 3 branches) and different target splitting rules (Entropy, Gini, and Probability Chi-square) have been applied on comprehensive survey data and to find out the best performing algorithm among the six models for tourism knowledge discovery. Results show that there are a various type of tourists with each group having different patterns or rules. This research study can be very helpful for tourist association, hospitality and hotel managers.