A Statistical Generation Method of Association Rules by Inference of Diagnostic Test Index in Data Mining

Abstract

In this paper, we investigated the possibility of using Youden s index, which is a diagnostic test index used in diagnostic medicine, as an interestingness measure among objective thresholds in association rules. First, we looked at the relationship of confidence measures for the sensitivity and specificity that constitute the Youden index. We also examined the relationship between Youden index and confidence measures by expressing the Youden index formula using various types of confidence, and presented the confidence interval of the Youden index. In addition, after grasping the meaningful properties as interestingness measure, we proved whether the condition of interestingness measure proposed by Piatetsky-Shapiro (1991) was satisfied. By simulation study, if the lower limit of the Youden index has a positive value, it is concluded that there is a positive association rule. If the upper limit of the Youden index has a negative value, it is determined that there is a negative association. If the lower limit of the Youden index has a negative value but the upper limit is positive, then it would be desirable to reserve whether or not there is a association rule.