Predict And Associate Tourist Preference Patterns, The Case Of Ministry Of Culture And Tourism Of Ethiopia
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Date
2015-06-05
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Addis Ababa University
Abstract
Tourism is one of the largest and rapidly growing industries in Ethiopia. It has a vital influence on economic development of a country. The degree of tourist attraction of Ethiopia to the others east African countries is, not strong enough to penetrate the tourism market. The sector faces immense challenges, because of its intangible nature and lack of understanding tourist preference.
This study attempts to identify the major determinants factors to predict tourist preference in Ethiopia. This research used the CRISP data mining methodology and 10484 tourist data were taken from Ministry of Culture and Tourism of Ethiopia for experiment that was collected from 2008 - 2012.
Experiments conducted using the J48 decision tree and Naïve Bayes algorithm for classification and the Apriori algorithm of association rule in Waikato environment for knowledge analysis (WEKA). J48 decision tree algorithm with the overall model accuracy of 94.8 % has offered interesting rules. The results of this study have showed that the data mining techniques are valuable for predicating tourist preference.
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Tourism, Data mining, Classification, and Association, Tourism, Data mining, Classification, and Association