Mining Unexpected Patterns by Decision Trees with Interestingness Measures

We believe that unexpected, interesting patterns may provide researchers with different visions for future research. In this study, we propose an unexpected pattern mining conceptual model that uses decision trees to compare the recovery rates of two different treatments and to find patterns that contrast with the prior knowledge of domain users. In the proposed model, we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of decision trees.