臺灣博碩士論文加值系統

傳統的K-最近鄰(K-Nearest Neighbors，簡稱KNN)分類演算法是使用一個固定的K值，由最相近的K個鄰居中，投票決定受測資料應歸屬於哪一個類別。然而，相關研究顯示，變動的K值可改善KNN的分類效果。因此，本研究在KNN分類演算法中，加入Local KNN及Fuzzy C-means歸屬程度值的概念，讓個別測試資料使用較適合其本身的K值，進而改善整體分類效果。

The K-nearest-neighbor algorithm traditionally predicts the class of a record based on the decision from the K nearest neighbors of the record, for a fixed K value. However, recent studies showed that using different K values for different records could improve the prediction accuracy. This study integrates Fuzzy C-means algorithm to assist determining a proper K value for each record in a local KNN algorithm. Performance results show this method outperforms the traditional KNN in term of prediction accuracy.

書名頁...............i
論文口試委員審定書.....ii
授權書..............iii
中文摘要.............iv
英文摘要..............v
致謝................vi
目錄................vii
表目錄..............ix
圖目錄...............x
第一章 緒論...........1
第一節 研究背景........1
第二節 研究動機........1
第三節 研究目的........2
第四節 論文架構........2
第二章 文獻探討........3
第一節 資料採礦........3
第二節 非監督式學習與監督式學習......4
第三節 K-means演算法............. 5
第四節 模糊C平均分群演算法(Fuzzy C-means Clustering Algorithm) ...5
第五節 網格與密度.................8
第六節 K最近鄰演算法(K-Nearest Neighbor Algorithm)...11
第七節 Local KNN演算法...........12
第八節 分類效能評估...............13
第三章 研究方法..................15
第一節 研究方法..................15
第二節 研究步驟..................23
第四章 實驗結果..................24
第一節 實驗資料..................24
第二節 實驗設計..................24
第三節 實驗結果..................24
第五章 結論與未來發展.............29
第一節 結論…....................29
第二節 未來發展..................30
參考文獻..........................31

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