English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 51296/86402 (59%)
造訪人次 : 8166425      線上人數 : 87
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107914

    題名: A GA-based approach for finding appropriate granularity levels of patterns from time series
    作者: Chen, Chun-Hao;Tseng, Vincent S.;Yu, Hsieh-Hui;Hong, Tzung-Pei;Yen, Neil Y.
    日期: 2016-01
    上傳時間: 2016-10-18 02:10:22 (UTC+8)
    出版者: Inderscience Publishers
    摘要: In our previous approach, we proposed an algorithm for finding segments and patterns simultaneously from a given time series. In that approach, because patterns were derived through clustering techniques, the number of clusters was hard to be setting. In other words, the granularity of derived patterns was not taken into consideration. Hence, an approach for deriving appropriate granularity levels of patterns is proposed in this paper. The cut points of a time series are first encoded into a chromosome. Each two adjacent cut points represents a segment. The segments in a chromosome are then divided into groups using the cluster affinity search technique with a similarity matrix and an affinity threshold. With the affinity threshold, patterns with the desired granularity level can be derived. Experiments on a real dataset are also conducted to demonstrate the effectiveness of the proposed approach.
    關聯: International Journal of Web and Grid Services 12(3), pp.217 - 239
    DOI: 10.1504/IJWGS.2016.079159
    顯示於類別:[資訊工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



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