| With the growing phenomenon of an aging population,Intelligent Health Surveillance technology has been developing rapidly.Meanwhile,as of things,the development of computer vision and other information technology to make rapid growth of Intelligent Health Surveillance data and diversified characteristics.Therefore,economic significance and the scientific value of the data has been an unprecedented increase.Mining association rules fully business and data,between data become the next hot spot for the Health Surveillance System to promote and applications.This thesis systematically analyzes the characteristics of intelligent system of health care data.It describes association rules and privacy protection of data analysis research status in detail.This thesis depth studies data association analysis for Intelligent Health Surveillance environment.Apriori algorithm adapted to the Intelligent Health Surveillance(AIHS)and Intelligent Health Surveillance data association rules algorithm based on privacy protection(ARP-IHS)were given.These methods provide a solution for the Intelligent Health Surveillance data analysis.Aiming at the problem that the mining efficiency of current Apriori association rules algorithm is too low.This thesis proposes a improved algorithm based on the Apriori algorithm for the Intelligent Health Surveillance(AIHS).This method stores each transaction set of Intelligent Health Surveillance items using 0 or 1.We can obtain the itemsets support of Intelligent Health Surveillance using the binary bit operation,then generating frequent itemsets based on minimum support.At last,generating strong association rules from the frequent itemsets.Compared with traditional methods,time efficiency of this method is higher than Apriori algorithm.This thesis analyzes the problems of privacy protection and information security faced by Intelligent Health Surveillance data.It proposes Intelligent Health Surveillance data association rules algorithm based on privacy protection(ARP-IHS).The method uses recurrence relations of inverse matrix and the simplified method for solving support with the first line element,and giving a formula for solving the first line element of the inverse matrix.This method eliminates the time-consuming process of inversion,So the time efficiency and the space efficiency of Intelligent Health Surveillance data association rules algorithm based on privacy protection is higher than the traditional random perturbation algorithm. |
