科學家利用基因微陣列來研究特定基因在不同細胞與組織中的表現差異量，期待了解疾病如癌症等的成因。然而，基因微陣列卻存在著高維度和小樣本的特性，所以在分類時，常會有過度配適、分類過程包含過多不相關基因…等的問題，而為了改善這些情況，基因選取便扮演了一個重要的角色。雖然近年來，已有許多的基因選取法被相繼提出，但是大多為個別基因選取法，存在基因共線性、缺乏組合基因考量…等問題。為了解決這個問題，劉冠良 (2007) 提出了以個別基因排序法結合群集演算法，進行相關基因選取和多餘基因剔除的方法，此方法雖然可以進行三個以上的組合基因替換，但是在演算法進行時，卻苦於無法精準設定使用者所需群數的這個參數，因而造成處理難度的增加，進而影響到了速度和穩定度的改善。本研究便以此為出發點，提出一個有效率的分群演算法，除了可以提升效能，也可以擁有穩定的分群結果，而這個穩定的分群結果也適合應用在基因微陣列上，作基因挑選和替換的動作。

Scientists use gene microarray (DNA microarray) data to study the differences among cells and tissues. They usually expect to find the origin causes of a specific disease. Due to the special characteristics of microarray data, high dimension and small number of samples, overfitting will generally occur, or noisy data may exist. Thus, gene selection plays an important role to solve the above problems. Though many gene selection methods have been proposed in these years, most of them are individual gene selection methods which may encounter the problems of gene collinearity and lack of consideration for combination genes. In order to solve those problems, an integration approach of individual gene ranking methods and clustering methods has been proposed to select a proper gene subset for classification. However, in such an integration approach, since the number of clusters cannot be a pre-specified parameter for clustering, its computational efficiency and stability are problematic. This research proposes an effective clustering method. After testing the clustering method on five microarray data sets, the experimental results show that it cannot only enhance the computational efficiency, but also generate stable clustering results. In addition, after applying our clustering method for gene selection, the resulting accuracies are close to the accuracies resulting from the original integration approach.

中文
劉冠良 (2007)，以叢集分析與距離測度為基礎之基因選取法，國立成功大學資訊管理研究所碩士班碩士論文。

英文
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