臺灣博碩士論文加值系統

自組映射圖在資料探勘中是一種被廣為使用的技術，能將大量高維度資料透過視覺化方式投射至二維平面，以呈現真實資料群聚現象，可供後續分析、探討資料本身的特性，提供商業上應用。然而傳統自組映射圖存在一些問題：映射圖大小需預先決定，無法動態擴張大小；資料量太大時，可能會造成映射圖拓樸失真，無法完整呈現資料群聚現象；無法合理表達種類型資料間距離，造成拓樸結果不能反應真實資料分佈；應用在分類上，單一神經元內類別可能不一致，造成分類績效不佳。自適應結構自組映射圖是一種應用在分類上的方法，可以解決傳統自組映射圖固定神經元大小以及神經元內類別不一致問題，提高分類精確度；但是，無論是傳統自組映射圖或是自適應結構自組映射圖，對於種類型資料的距離，均無法合理表達。本研究提出延伸性自適應結構自組映射圖，其延伸自適應結構自組映射圖動態擴張神經元的概念，解決傳統自組映射圖大小固定問題；結合距離階層結構來合理表達種類型資料間相對距離，並在映射圖拓樸結果上提出新的視覺化方式，能更有利於分析資料的特性與類別的分佈情形。透過人工資料集及真實資料集的實驗結果證明，本研究所提方法在分類上能保有自適應結構自組映射圖的精確度，映射圖的拓樸結果除了能合理的表達種類型資料的群聚現象也能提供視覺化的統計資訊。

The self-organizing map (SOM) is a visualized technique, which has been extensively applied in data mining. The SOM can project high-dimensional data into low-dimensional space while preserving the original topological relation. However, traditional SOM fixes the structure of the map and can not dynamically expand neurons. Moreover, when used for classification traditional SOM usually obtained low accuracy. The structure adaptive self-organizing map (SASOM) solved the problem by dynamically splitting neurons to make all the data in a neuron have the same class label. However, SOM and SASOM can not reasonably deal with categorical values. In this paper, we propose a Generalized SASOM (GSASOM) to classify mixed, numeric and categorical, data, and to visualize the distribution of data with class labels. Our model integrates the ideas from SOM, GSOM, and SASOM, and overcomes their shortcomings. The experimental results confirm that our method maintains classification accuracy compared to that of SASOM. When categorical data are involved in the training data, GSASOM can faithfully preserve the topological order, and also can provide more useful visualized information.

摘要.........................................i
ABSTRACT....................................ii
誌謝.......................................iii
目錄........................................iv
表目錄......................................vi
圖目錄.....................................vii
一、緒論.....................................1
1.1研究背景與動機............................1
1.2研究目的..................................2
1.3研究範圍及限制............................3
1.4論文架構..................................3
二、文獻探討.................................4
2.1 自組映射圖...............................4
2.2 漸增式自組映射圖類型.....................5
2.3 階層式自組映射圖類型.....................7
2.4 自適應結構自組映射圖.....................9
2.5 延伸性自組映射圖........................10
2.6 小結....................................11
三、研究架構................................12
3.1 表達種類型資料距離......................12
3.2 延伸性自適應結構自組映射圖..............15
3.3 初始化子神經元向量權重..................17
3.3.1中心點距離計算範例.....................20
3.4 分類正確率評估方法......................23
3.5 視覺化映射圖分類結果....................23
四、實驗....................................26
4.1 人工資料集..............................26
4.2 真實資料集..............................29
4.2.1 比較映射圖初始大小對分類績效影響......30
4.2.2 比較不同方法分類績效..................30
4.2.3 比較不同方法視覺化拓樸結果............32
五、結論....................................34
5.1 結論....................................34
5.2未來研究.................................34
參考文獻....................................35

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