Tree-based frequent itemsets mining for analysis of life-satisfaction and loneliness of retired athletes

Abstract

Life-satisfaction and loneliness are two key indicators of individual mental state, and their detailed analysis could help improve the resettlement policy of retired athletes. This paper proposes a tree-based frequent itemsets mining method to estimate the influence factors of the life-satisfaction and the loneliness of retired athletes. The basic situations of the retired athletes are collected by the questionnaires and transformed into the binary attributes. Then, an extend prefix tree is built for mining the frequent itemsets. The lift measure is employed to generate the association rules based on the obtained frequent itemsets and realize the rules prune. The actual survey data of 750 Chinese retired athletes are adopted for comparing the proposed method and the Apriori algorithm. Experimental results verify the effectiveness of the proposed method is higher. Moreover, the obtained rules show that the health condition, the education, the social insurance participation affect both the life-satisfaction and the loneliness of retired athletes, and the income only affect the life-satisfaction of retired athletes.