淡江大學機構典藏:Item 987654321/108290
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62822/95882 (66%)
Visitors : 4019179      Online Users : 1046
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/108290


    Title: Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis
    Authors: Ming-Yang Chang;Rui-Dong Chiang;Shih-Jung Wu;Chien-Hui Chan
    Keywords: Treatment comparison;Unexpected patterns;Domain-driven data mining;Interestingness measures
    Date: 2016-10
    Issue Date: 2016-11-22 02:10:27 (UTC+8)
    Publisher: Springer Berlin Heidelberg
    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 20(10), p.3991–4003
    DOI: 10.1007/s00500-015-1735-0
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML313View/Open
    Mining unexpected patterns using decision trees and interestingness measures a case study of endometriosis.pdf1014KbAdobe PDF3View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback