comparison of decision tree and naïve bayes methods in classification of researcher’s cognitive styles in academic environment

in today world of internet, it is important to feedback the users based on what they demand. moreover, one of the important tasks in data mining is classification. today, there are several classification techniques in order to solve the classification problems like genetic algorithm, decision tree, bayesian and others. in this article, it is attempted to classify researchers to “expert” and “novice” based on cognitive style factors in order to have as best as possible answers. the domain of this research is based on academic environment. the critical point of this study is to classify the researchers based on decision tree and naïve bayes techniques and finally select the best method based on the highest accuracy of each method to help the researchers to have the best feedback based on their demands in the digital libraries.