English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 49064/83170 (59%)
造訪人次 : 6963584      線上人數 : 76
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/987654321/17388


    題名: An Algorithm for Mining Strong Negative Fuzzy Sequential Patterns
    作者: Lin, Nancy P.;Hao, Wei-hua, Chen, Hung-jen;Chang, Chung-i;Chueh, Hao-en
    貢獻者: 淡江大學資訊工程學系;軍訓室
    關鍵詞: Fuzzy itemset;sequential pattern;fuzzy sequential pattern;negative sequential pattern
    日期: 2007-03
    上傳時間: 2013-06-07 10:46:58 (UTC+8)
    出版者: Braga: North Atlantic University Union
    摘要: Many methods have been proposed for mining fuzzy sequential patterns. However, most of conventional methods only consider the occurrences of fuzzy itemsets in sequences. The fuzzy sequential patterns discovered by these methods are called as positive fuzzy sequential patterns. In practice, the absences of frequent fuzzy itemsets in sequences may imply significant information. We call a fuzzy sequential pattern as a negative fuzzy sequential pattern, if it also expresses the absences of fuzzy itemsets in a sequence. In this paper, we proposed a method for mining negative fuzzy sequential patterns, called NFSPM. In our method, the absences of fuzzy itemsets are also considered. Besides, only sequences with high degree of interestingness can be selected as negative fuzzy sequential patterns. An example was taken to illustrate the process of the algorithm NFSPM. The result showed that our algorithm could prune a lot of redundant candidates, and could extract meaningful fuzzy sequential patterns from a large number of frequent sequences.
    關聯: International Journal of Computers 3(1), pp.167-172
    DOI: 
    顯示於類別:[軍訓室] 期刊論文
    [資訊工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    index.html0KbHTML283檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

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