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研究生:陳信夫
研究生(外文):Hsin-fu Chen
論文名稱:基於字詞關係動態建立階層分群
論文名稱(外文):Dynamic Hierarchical Clustering Based on Taxonomy
指導教授:林熙禎林熙禎引用關係
指導教授(外文):Shi-jen Lin
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊管理研究所
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:2011
畢業學年度:99
語文別:中文
論文頁數:58
中文關鍵詞:階層分群演算法、動態分群演算法、分類學 、文件分群
外文關鍵詞:Dynamic clustering algorithmHierarchical clusteringTaxonomy
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資訊爆炸時代的來臨,越來越多使用者在網路上搜尋相關資料進行閱讀。本研究目標是將大量文件資料進行階層分群(Hierarchical Clustering),並以字詞關係建置具有上下包含關係的分類學(Taxonomy),以用來成為階層群集的標籤。運用上,能方便使用者快速瞭解文件集有哪些主題,迅速選擇所需主題的文件進行閱讀。本研究提出的系統架構有效地改善了階層群集研究上的五個議題:高維度的向量、動態的特徵選取與文件分群、文件處理順序、文件跨領域分群與群集標籤之間的關係。
With the popularity of Internet, the World Wide Web contains a giant amount of information. To search relevant information from large number of texts becomes a challenge to the users. Hierarchical clustering is one of the methods to conquer this problem. Because its features let users can browse the topic gradually and find out the most relevant documents they have interesting. But there are still have some challenge in hierarchical clustering must be addressed, like high dimensionality of the data, dynamic data sets, the sensitivity of input order, documents has several concept, and the relationship of clusters and tags.
In this paper, we propose an approach of dynamic hierarchical clustering based on taxonomy to conquer those challenges. The experimental result shows that our method not only suitable for constructing hierarchical clustering in dynamic data sets, but also offer a easier structure to browse than traditional algorithms, BKM and UPGMA. In addition, the clusters are labeled meaningful tags with the relationship of containment can let users understand the whole concept of clusters rapidly.
摘 要 i
Abstract ii
誌 謝 iii
目 錄 iv
圖目錄 vii
表目錄 ix
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究方法 3
1.4 論文架構 4
第二章 文獻探討 5
2.1 特徵選取 5
2.1.1 詞彙頻率(Term Frequency, TF) 5
2.1.2 詞彙頻率與反向文件頻率(TF-IDF) 5
2.1.3 高頻項目集(Frequent Itemset) 7
2.1.4 資訊關聯(Mutual Information) 7
2.1.5 正規化谷歌距離(NGD, Normalized Google Distance) 8
2.2 分群演算法 9
2.2.1 切割式群集演算法 10
2.2.2 凝聚式階層分群演算法(Agglomerative Hierarchical Clustering) 11
2.2.3 分裂式階層分群演算法(Divisive Hierarchical Clustering) 13
2.3 分類學 15
2.3.1 詞彙句法樣式法(Lexico-syntactic Patterns) 16
2.3.2 機器可讀字典(Machine-readable Dictionaries) 17
2.3.3 資訊理論 (Information Theory) 18
2.4 小結 18
第三章 系統設計與架構 19
3.1 系統架構 19
3.2 資料前處理 20
3.2.1 Part-of-speech and word combination 20
3.2.2 The length of the word 21
3.2.3 The number of Google search results 21
3.2.4 NGD Calculate 22
3.2.5 Ranking and Filtering 23
3.3文件概念分群 24
3.3.1 Updated Beta-similarity Graph 25
3.3.2 Updated Max-S Graph 26
3.3.3 Updated Star Cover 27
3.4 建置分類學 28
3.4.1 NGD Calculate 28
3.4.2 Conditional Probability Calculate 29
3.4.3 BTRank 30
3.5文件階層分群 33
第四章 實驗結果與討論 36
4.1 資料集介紹 36
4.1.1 Wikipedia(維基百科) 36
4.1.2 MeSH(Medical Subject Headings) 37
4.1.3 Painters and Paintings 38
4.1.4 資料集與實驗的對應 39
4.2 評估方法 39
4.2.1 F1 score 39
4.2.2 Fβ score 40
4.2.3 FCubed 41
4.3 資料前處理實驗結果 43
4.4 建置分類學實驗結果 44
4.5 文件概念分群與文件階層分群實驗結果 46
4.6 階層結構分析 48
4.7 系統效能分析 49
4.7.1 時間複雜度 49
4.7.2 系統總體時間分析 50
第五章 結論與未來研究方向 52
5.1 結論 52
5.2 未來研究方向 53
參考文獻 55
中文部分 55
英文部分 55
網頁部分 58
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2. 張家寧(民98),以概念萃取為基礎之文件分群與視覺化,未出版碩士論文,國立交通大學資訊科學與工程研究所。
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