研究生: |
林冠樺 Lin, Kuan-Hua |
---|---|
論文名稱: |
考量使用者觀點之半監督式分群演算法 Semi-Supervised Clustering with Perception Vectors |
指導教授: | 吳尚鴻 |
口試委員: |
陳銘憲
陳良弼 黃俊龍 |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 資訊工程學系 Computer Science |
論文出版年: | 2014 |
畢業學年度: | 102 |
語文別: | 英文 |
論文頁數: | 26 |
中文關鍵詞: | 分群 、半監督式分群 、個人化分群 、資料探勘 、使用者觀點 |
外文關鍵詞: | clustering, semi-supervised clustering, personalized clustering, data mining, user perception |
相關次數: | 點閱:2 下載:0 |
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傳統的分群演算法只考慮到資料節點間的相似性,並無法達到個人化分群的功能,於是允許使用者提出旁側資訊的半監督式分群演算法被提出。在本篇論文中,我們發現即使有了旁側資訊的幫助,半監督式分群演算法所找到的結果和使用者心中所想的分群仍然存在著巨大的落差,造成此特性的主要原因為取樣偏誤—傳統旁側資訊可能只包含少數非隨機抽樣之節點,於是誤導演算法找出錯誤的分群結果。為了克服這個難題,我們提出了從使用者觀點學習之方式,請使用者提供觀點向量,其中每個向量敘述了使用者對於每一個群體的概念,並從這個角度提出了一個同時考慮傳統旁側資訊及使用者觀點向量之演算法,名為 BiLinear Embedded Perception (BLEP) clustering。BLEP 分群演算法可以學習到每個群體的隱性變量,進而找到更精確的結果。我們利用眾包平台蒐集許多不同使用者觀點之分群,並在此資料組上進行實驗,並對 BLEP 演算法之結果以及效能做更深入的討論。
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