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研究生:鄭雅文
研究生(外文):Ya-Wen Cheng
論文名稱:以正規概念分析為基礎結合隸屬函數之重疊式分群法
論文名稱(外文):Uncertain Memberships Based Overlapping Clustering Methods Using Formal Concept Analysis
指導教授:呂瑞麟呂瑞麟引用關係
指導教授(外文):Jui-Lin Lu
口試委員:陳宜惠陳博智
口試委員(外文):Yi-Hui ChenPo-Chi Chen
口試日期:2016-07-25
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊管理學系所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:32
中文關鍵詞:資料探勘重疊式分群法正規概念分析
外文關鍵詞:data miningoverlapping clusteringFormal Concept Analysis
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分群(Clustering)是機器學習與資料探勘中主要的研究領域之一,目前分群已經應用在許多不同的領域,包括商業行銷、資料檢索、網路分析、生物科學等領域。分群可以透過相似度的計算,將相似的物件歸為同一群集。在重疊式分群法的研究中,允許一個物件同時屬於多個群集,相對的,單分群的研究中,只允許一個物件屬於一個群集,在過去分群法的研究中,大多數都是屬於單分群演算法,而現實生活中有許多應用都是屬於重疊式分群,例如一篇新聞可能同時在討論生活、運動或健康的主題。本研究參考Chen等人,以正規概念分析(Formal Concept Analysis, FCA)為基礎輔助重疊式分群,藉由FCA找出具有相同特徵的物件,由於具有相同特徵的物件很有可能是在描述類似的主題,因此先利用正規概念將這些物件聚集在一起,此外,由於一個物件可能屬於多個概念,若以正規概念取代原物件進行分群,便可以達到重疊式分群法的效果。但傳統FCA屬於明確集合,並不能了解物件屬於概念的程度,因此本研究結合隸屬函數,將概念轉換為模糊正規概念,來了解正規概念與物件之間的隸屬程度,改善傳統FCA的限制。實驗結果證實本研究所提出的兩個方法可以擁有較過去重疊式分群法優秀的分群結果。

中文摘要 i
英文摘要 ii
目錄 iii
圖目錄 iv
表目錄 v
1 緒論 1
1.1 研究背景 1
1.2 研究目的 1
1.3 論文架構 3
2 文獻探討 4
2.1 Hard Memberships Based-Methods 4
2.2 Uncertain Memberships Based-Methods 5
3 研究方法 7
3.1 特徵選取 7
3.2 正規概念分析 7
3.3 模糊正規概念矩陣 10
3.3.1 Fuzzy Concept-Object Matrix 10
3.3.2 Fuzzy Concept-Feature Matrix 13
3.4 模糊正規概念過濾 15
3.5 初始分群結果 16
4 實驗與結果分析 18
4.1 實驗資料集 18
4.2 驗證方法 19
4.3 分群結果 20
4.3.1 實驗一:Reu-Te資料集 21
4.3.2 實驗二:Rue-Tr資料集 23
5 結論與未來研究方向 27

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