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 資料分群為探勘技術重要研究課題之一，其過程需考慮分群組數與資料分群組態之群中心。大部分資料分群演算法都是事先須給予固定的群數，而動態分群是指在未給定組數下，透過演算法進行資料分群，自動找到最佳分群組數與資料分群組態之群中心。本研究運用差分演化演算法 ( Differential evolution ) 演算法(以下簡稱差分演算法)，進行動態資料分群，稱為差分動態分群演算法( Dynamic clusteringusing differential evolution, DCDE )。本演算法每個世代內，先使用常態分配抽樣決定解向量的分群組數，接著使用差分演算法更新每個解向量的群中心值，再結合分群效度指標 ( Cluster validity index ) 的衡量進行資料動態分群結果，使得各解向量不斷地往最佳組數之子空間移動。本研究利用八個人工資料與四個UCI 資料集進行測試，實驗結果顯示，DCDE 演算法在未知群數下，確實能有效的找到各組資料的最佳群數，同時得到較佳且穩定的分群組態之群中心。
 Data clustering is one of the important issues on the data mining techniques which is the process of considering as the number of cluster and the center of cluster. Most of data clustering algorithms are prior known of the number of cluster but dynamic clustering is able to find the optimal number of cluster and center of cluster dynamically by algorithms. This research is used differential evolution algorithm to perform data clustering which is called as dynamic clustering using differential evolution (DCDE). This algorithm is accessed the number of cluster of solution vectors first by normal distribution and then updating the center of cluster of every solution vectors by differential evolution. Finally, we combine cluster validity index to estimate the results of dynamic clustering to make the solution vectors move to the optimal number of cluster of the subspace constantly. This paper uses eight artificial data sets and four real-world data sets to test. The experimental results show that DCDE is able to find the accurate number of cluster and better and more stable center of cluster with unknown the accurate number of cluster.
 目 錄中文摘要iiAbstarctiii致謝 iv目錄 v圖目錄 vii表目錄 viii第一章 緒 論 91.1 研究背景與動機 91.2 研究目的 101.3 研究範圍與限制 111.4 研究流程 121.5 論文架構 14第二章 文獻探討 162.1 資料分群 162.1.1 階層法 162.1.2 分割法 162.1.3 密度基準法 162.1.4 模式基準法 182.1.5 網格基準法 182.2 k-means演算法 182.3 粒子演算法 192.4 GCUK演算法 202.5 DCPSO演算法 202.6 差分演算法 212.6.1 DE演算法流程 232.7 分群效度演算法 23第三章 研究方法 273.1 DCDE演算法模型 273.1.1 資料分群數學模式 283.1.2 常態分佈模型 283.2 DCDE演算法介紹 303.2.1 DCDE演算法簡介 303.2.2 參數初始 323.2.3 解的表達 333.2.4 演算法過程 333.2.5 演算法說明 343.3 避免不合理解( Infeasible solution )的處理 36第四章 DCDE演算法範例說明 384.1 DCDE演算法流程圖 384.2 DCDE演算法範例說明 38第五章 實驗結果與評估 465.1 測試環境與資料集 . 465.2 參數測試與實驗參數設定 485.3 DCDE演算法與GCUK，DCPSO演算法比較最佳群數 505.4 綜合演算法求解品質比較 515.5 綜合演算法收歛趨勢比較 545.6 問題大小對DCDE演算法之影響 57第六章 結論與未來研究 586.1 結論 586.2 未來研究 58參考文獻 60附錄A人工資料實驗之資料集合及散佈圖 64
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International Conference on, 2006 Page(s):232 - 235
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 1 結合K-means及差分演化法之入侵偵測研究 2 差分演算法實作軟體工作量預估之研究 3 以鏈結式及熵值為基礎的分群演算法 4 以啟發式方法求解具時窗限制多場站車輛巡迴路線問題 5 結合FCM及PSO處理動態模糊分群問題 6 差分演化演算法應用於單元形成問題 7 以差分進化演算法解決自動資料分群問題

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