An Extension of Apriori Algorithm to Discover Individualized Treatment Optimization of Breast Cancer

Normally there is the very huge dataset in the application of medicine and bioinformatics. Traditional association algorithm produces too many rules in this kind of application, which are difficult to be identified and compared. In this work we attempt to propose an extension of Apriori algorithm to explore individualized treatment optimization of breast cancer. As the result of our method, the comparative association rules are produced. Thus, association rules algorithm become more practical and useful, especially in the field of medicine and bioinformatics.