English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 61880/94640 (65%)
造访人次 : 1638032      在线人数 : 28
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    淡江大學機構典藏 > 行政單位 > 軍訓室 > 會議論文 >  Item 987654321/18161

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/18161

    题名: An Adaptive Crossover-Imaged Clustering Algorithm
    作者: Lin, Nancy P.;Chang, Chung-i;Chen, Hung-jen;Chueh, Hao-en;Hao, Wei-hua
    贡献者: 淡江大學資訊工程學系;淡江大學軍訓室
    关键词: Data Mining;Grid Structure;Crossover Image;Significant Cell;Deflected Grid
    Data Mining;Grid Structure;Crossover Image;Significant Cell;Deflected Grid
    日期: 2007-09-01
    上传时间: 2009-08-13 11:26:41 (UTC+8)
    出版者: World Scientific and Engineering Academy and Society (WSEAS)
    摘要: The grid-based clustering algorithm is an efficient clustering algorithm, but its effect is seriously influenced by the size of the predefined grids and the threshold of the significant cells. The data space will be partitioned into a finite number of cells to form a grid structure and then performs all clustering operations on this obtained grid structure. To cluster efficiently and simultaneously, to reduce the influences of the size of the cells and inherits the advantage with the low time complexity, an Adaptive Crossover-Imaged Clustering Algorithm, called ACICA, is proposed in this paper. The main idea of ACICA algorithm is to deflect the original grid structure in each dimension of the data space after the image of significant cells generated from the original grid structure have been obtained. Because the deflected grid structure can be considered a dynamic adjustment of the size of original cells and the threshold of significant cells, the new image generated from this deflected grid structure will be used to revise the originally obtained significant cells. Hence, the new image of significant cells is projected on the original grid structure to be the crossover image. Finally the clusters will be generated from this crossover image. The experimental results verify that, indeed, the effect of ACICA algorithm is less influenced by the size of the cells than other grid-based algorithms. Finally, we will verify by experiment that the results of our proposed ACICA algorithm outperforms than others.
    關聯: Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization (SMO’07), pp.213-218
    显示于类别:[軍訓室] 會議論文
    [資訊工程學系暨研究所] 會議論文


    档案 大小格式浏览次数



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