The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance

Revealing valuable knowledge hidden in corporate data becomes more critical for enterprise decision making. When more data is collected and accumulated, extensive data analysis would not be easier without effective and efficient data mining methods. This paper proposes a hybrid of the association rule algorithm and genetic algorithms (GAs) approach to discover a classification tree. The association rule algorithm is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. In addition an association rule algorithm is employed to acquire the insights for those input variables most associated with the outcome variable before executing the evolutionary process. These derived insights are converted into GA's seeding chromosomes. The proposed approach is experimented and compared with a regular genetic algorithm in predicting a student's course performance.