Mining association rules from data with hybrid attributes based on immune genetic algorithm
Guangjun Yang · 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery · 2010
Extracting association rules from data with both discrete and continuous attributes is an important problem in KDD. A new model of immune genetic algorithm is formulated for solving this problem. This algorithm uses three-segment chromosomes, integrating the discretization, attributes reduction and mining association rules. And immune mechanism is introduced into genetic algorithm to avoid premature phenomenon and improve the efficiency of GA. The results of experiments prove the correctness and validity of the algorithm.