| Summary: | 碩士 === 國立政治大學 === 統計學系 === 105 === The current study used the two clustering algorithms in unsupervised learning to explore adolescents’ risk-taking behaviors cross-culturally. The first algorithm was data cloud geometry tree, which considered two elements, temperature and time, in the algorithm. Through the filtering of temperature and the automatic detection of time axis, the differences between clusters were increased as temperature was lowered. The second algorithm was agglomerative hierarchical clustering, a simple and practical method. The risk-taking data were divided into two parts: numerical type and categorical type. Hypothesis tests were conducted to verify whether the differences between groups were significant. The results showed that the hierarchical clustering method performed better. In addition, the findings showed that the group differences in the special cluster were larger when using the data cloud geometry tree. Finally, the difference between the special group and the non-special group was calculated, and the risk value of the special group was high, which identified the potentially high-risk adolescents. The special clusters obtained from the two algorithms were compared to get the repeated subjects, which served as our target. Also, demographic data of the target were discussed.
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