Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis
學年 105
學期 1
出版（發表）日期 2016-10-01
作品名稱 Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis
作品名稱（其他語言）
著者 Ming-Yang Chang; Rui-Dong Chiang; Shih-Jung Wu; Chien-Hui Chan
單位
出版者
著錄名稱、卷期、頁數 Soft Computing 20(10), p.3991–4003
摘要 Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare 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 that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.
關鍵字 Treatment comparison;Unexpected patterns;Domain-driven data mining;Interestingness measures
語言 en
ISSN 1432-7643
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/112503 )

SDGS 良好健康和福祉,產業創新與基礎設施