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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/103555


    Title: Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis
    Authors: Chang, Ming-Yang;Chiang, Rui-Dong;Wu, Shih-Jung;Chan, Chien-Hui
    Keywords: Treatment comparison · Unexpected patterns · Domain-driven data mining · Interestingness measures
    Date: 2015-06
    Issue Date: 2015-08-25 14:50:24 (UTC+8)
    Publisher: Heidelberg: Springer
    Abstract: 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.
    Relation: Soft Computing
    DOI: 10.1007/s00500-015-1735-0
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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