English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49299/83867 (59%)
Visitors : 7160599      Online Users : 59
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
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/75801


    Title: A Novel Method for Mining Temporally Dependent Association Rules in Three-Dimensional Microarray Datasets
    Authors: Liu, Yu-cheng;Lee, Chao-hui;Chen, Wei-chung;Shin, J. W.;Hsu, Hui-huang;Tseng, Vincent S.
    Contributors: 淡江大學資訊工程學系
    Keywords: Data Mining;Microarray;Gene Expression Analysis;Association Rule Mining
    Date: 2010-12
    Issue Date: 2012-04-16 09:42:50 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers(IEEE)
    Abstract: Microarray data analysis is a very popular topic of current studies in bioinformatics. Most of the existing methods are focused on clustering-related approaches. However, the relations of genes cannot be generated by clustering mining. Some studies explored association rule mining on microarray, but there is no concrete framework proposed on three-dimensional gene-sample-time microarray datasets yet. In this paper, we proposed a temporal dependency association rule mining method named 3D-TDAR-Mine for three-dimensional analyzing microarray datasets. The mined rules can represent the regulated-relations between genes. Through experimental evaluation, our proposed method can discover the meaningful temporal dependent association rules that are really useful for biologists.
    Relation: Proc. 2010 International Computer Symposium, pp. 759-764
    DOI: 10.1109/COMPSYM.2010.5685410
    Appears in Collections:[資訊工程學系暨研究所] 會議論文

    Files in This Item:

    File Description SizeFormat
    A Novel Method for Mining Temporally Dependent Association Rules in.pdf全文檔363KbAdobe PDF234View/Open
    index.html0KbHTML153View/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