教師資料查詢 | 類別: 期刊論文 | 教師: 武士戎 WU, SHIH-JUNG (瀏覽個人網頁)

標題: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
語言英文
ISSN1432-7643
期刊性質國外
收錄於SCI;EI;
產學合作
通訊作者
審稿制度
國別美國
公開徵稿
出版型式,電子版,紙本
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