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研究生:林彥廷
研究生(外文):Yen-Tin Lin
論文名稱:以資料分佈為基礎的非監督式聚類分割法
論文名稱(外文):An unsupervised clustering approach based on its data distribution
指導教授:楊英魁楊英魁引用關係
指導教授(外文):Ying-Kuei Yang
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:96
中文關鍵詞:非監督式聚類演算法圖形識別模糊C均值演算法間距
外文關鍵詞:unsupervised clustering algorithmpattern recognitionFuzzy c-Meansgap
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本文的主要目的是提出一個有效的非監督式聚類演算法,考量不同資料點間所存在的間距來做為各個聚類可能的聚類分割範圍,再配合所建立的評估函數,計算同一聚類內資料點的聚合度函數及不同聚類中心間資料點的分離度函數以得知聚類分類的優劣程度,做為選擇適當的聚類分割處之依據以執行分類。此法與人類的分類方式一樣,在不須要預先指定聚類的數目下,就能夠對一組欲分類的資料進行分類。最後為了試驗本文所提出的方法,總共模擬與分析了十個不同特性的資料樣本。在模擬中,除了將這十個試驗樣本以本文所提的演算法執行分類,並且也將Fuzzy c-Means演算法之分類結果列出,做為對照之用。模擬結果顯示,本文所提出的方法比Fuzzy c-Means演算法擁有更高的正確性及適用性。

In this thesis, a new effective unsupervised clustering algorithm is presented in which the best clustering scopes are based on both the distribution of given data points and an evaluation function that includes modified compactness and separation functions. The proposed method simulates human’s behavior of clustering process that effectively clusters an arbitrarily distributed data set without priori knowledge of the number of clusters in the data set. This algorithm has been implemented, analyzed and tested on ten data sets. The experimental results show that the proposed algorithm has better performance than Fuzzy c-Means clustering algorithm. The proposed algorithm is also much simpler than other unsupervised clustering algorithms proposed so far to achieve good clustering results by eliminating required factors that need to be set up objectively by users.

第1章 緒論1
1.1 研究背景1
1.2 研究目的3
1.3 論文架構4
第2章 聚類與聚類方法5
2.1 識別能力5
2.2 聚類法則6
2.3 非監督式的聚類演算法7
2.3.1 K個最鄰近成員法8
2.3.2 模糊C均值演算法13
2.3.3 其他相關的聚類演算法19
2.3.3.1 加速模糊C均值聚類法19
2.3.3.2 分段式模糊C均值聚類法20
2.3.3.3 基因式模糊C均值聚類法21
2.3.3.4 模糊凸形聚類法21
2.3.3.5 非監督式模糊聚類法22
2.3.3.6 模糊類神經網路法23
2.4 本章結論24
第3章 以資料分佈為基礎的聚類演算法25
3.1 演算法構想25
3.2 聚類的範圍與聚類中心位置之決定26
3.3 資料間距的計算38
3.4 演算法之效度評估函數40
3.4.1 聚合度函數41
3.4.2 分離度函數47
3.4.3 目標函數52
3.5 演算法的步驟說明53
3.5 本章結論59
第4章 模擬結果分析60
4.1 試驗方法60
4.2 模擬結果61
4.3 本章結論90
第5章 結論92
參考文獻95

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