博碩士論文 etd-0907109-164128 詳細資訊


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姓名 歐陽正(Jeng Ouyang) 電子郵件信箱 E-mail 資料不公開
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 97學年第2學期
論文名稱(中) 一個以相似度為基礎的資料縮減方法
論文名稱(英) A Similarity-based Data Reduction Approach
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    摘要(中) 由於資訊技術的快速成長,所需處理的資料數量也急遽增加,因此設計一個有效的資料縮減方法是相當重要的一件任務,這也是本篇論文的核心。在本論文裡,我們提出一個以相似度為基礎的自建構式模糊分群演算法來進行資料縮減。此自建構式模糊分群演算法根據資料在統計上的特性,將相似的資料歸為同一個群聚。當所有的資料被輸入至此演算法一遍即可完成分群,並得到每一個群聚的平均值和標準差,而這些平均值的集合就是資料萃取後的結果。最後使用這些少量的新代表點來取代原始的大量資料。此演算法有兩個最大的優點,第一是速度較快且對於記憶體的需求較低。第二是使用者不必事先決定要取出多少代表點。在實驗的部份,我們也以多組真實的資料去驗證此演算法在速度上比其他的方法快而且有更好的縮減率,並透過三種分類器來測試我們所提方法之資料萃取效果。
    摘要(英) Finding an efficient data reduction method for large-scale problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to do the sampling of instances for the classification task. Instances that are similar to each other are grouped into the same cluster. When all the instances have been fed in, a number of clusters are formed automatically. Then the statistical mean for each cluster will be regarded as representing all the instances covered in the cluster. This approach has two advantages. One is that it can be faster and uses less storage memory. The other is that the number of new representative instances need not be specified in advance by the user. Experiments on real-world datasets show that our method can run faster and obtain better reduction rate than other methods.
    關鍵字(中)
  • 大型資料
  • 模糊相似度
  • 資料分群
  • 資料過濾
  • 資料萃取
  • 資料取樣
  • 資料縮減
  • 關鍵字(英)
  • fuzzy similarity
  • Large-scale dataset
  • data reduction
  • prototype reduction
  • instance-filtering
  • instance-abstraction
  • 論文目次 目錄
    摘要 i
    Abstract ii
    目錄 iii
    圖目錄 iv
    表目錄 v
    第一章 導論 1
    第二章 文獻探討 4
    2.1資料縮減方法 4
    2.2分類器模型 7
    第三章 研究方法 11
    3.1自建構式模糊分群演算法 11
    3.2範例 16
    第四章 實驗與結果 20
    4.1資料描述 20
    4.2實驗一 21
    4.3實驗二 25
    4.4實驗三 26
    4.5實驗四 32
    第五章 結論 35
    參考文獻 36
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  • 口試日期 2009-07-23 繳交日期 2009-09-07

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