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

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 44-47)
• Pages: 2085-2088
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.44-47.2085
• Online since: December 2010
• Authors: Qi Fan, Chang Jie Zhu, Liu Yin, Xiao Lu Xu, Feng Rong, Jian Yu Xiao, Bao Hua Wang
• Keywords: Apriori, Association Rule, Breast Cancer, Data Mining (DM), Individualized Treatment Optimization, SEER

References

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