臺灣博碩士論文加值系統

分群演算法（clustering algorithms）是一種非監督式的資料探勘方法，可以依照資料的分布情況，將性質相似的資料分成若干個群聚，再針對不同的群聚資料加以分析。
許多研究顯示Fuzzy C-means (FCM)在分群問題上是一個非常好的工具，利用模糊邏輯的概念，使分群方法的效果能夠提升。近年來，有許多文獻中，都依據FCM演算法做進一步的推廣，如:AFCM、CFCM、GK，而在本研究引入了無參數加權特徵萃取(NWFE) Weighted mean取代FCM 群中心的概念，提出了一個新模糊分群演算法的目標函數。另外本研究針對分群個數的評估，引入特徵萃取法LDA、NWFE的概念，提出了二個新的分群適切性指標。
實驗中，利用模擬資料與真實資料進行效能評估，新的模糊分群演算法和新的分群適切性指標，皆有不錯的效果。

Many researches show the fuzzy c-means clustering is a powerful tool for partitioning samples into different categories. However, the cost function of the classical fuzzy c-means (FCM) is defined by the distances from data to the cluster centers with their fuzzy memberships. In this study, a new fuzzy clustering algorithm, namely the fuzzy weighted c-means (FWCM), is proposed. In this proposed FWCM, the concept of weighted means used in nonparametric weighted feature extraction (NWFE) is employed for replacing the cluster centers in the FCM. The experiments on both synthetic and real data show that the proposed clustering algorithm can generate better clustering results than FCM and tradition the fuzzy clustering algorithms.

摘要 I
目錄 III
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 2
第二章 文獻探討 3
第一節 模糊分群演算法 3
壹、 改變距離度量方式 5
貳、 改變隶屬度約束條件 8
參、 改變隶屬度約束條件 8
肆、 目標函數加入熵 10
第二節 無參數加權特徵萃取 11
第三節 分群適切性指標 13
壹、 非模糊型適切性指標(Crisp clustering validity index) 13
貳、 模糊型適切性指標(Fuzzy Clustering Validity Index) 17
第三章 研究方法 20
第一節 資料描述 20
壹、 UCI資料 20
貳、 高光譜影像資料實驗 20
參、 教育測驗資料 23
第二節 研究方法 25
壹、 Fuzzy Weighted C-Means 25
貳、 分群適切性指標 28
第三節 實驗設計、資料 33
第四章 研究結果 35
第一節 實設計一結果 35
壹、 UCI資料集 35
貳、 影像資料集 35
參、 教育測驗資料 42
第二節 實設計二結果 44
第五章 結論與未來發展 47
參考文獻 48

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