由於資訊科技日新月異，新的群集分析方法不斷地被提出，但是大多分群方法都需要先設定欲分群之群數，因此本研究主要提出一自動核心分群之方法－Automatic Kernel Clustering with Bee Colony Optimization Algorithm (AKC-BCO)，不需事先決定欲分群的數目，可藉由資料的特徵值自行群聚成適合的群集，也可透過核心函數使得分類比傳統的歐式距離較為正確。
本研究先分別採用四組標竿資料集Iris、Wine、Glass以及Vowel來進行實驗，並與Dynamic Clustering using Particle Swarm Optimization (DCPSO)、Dynamic Clustering using Particle Swarm Optimization and Genetic Algorithm (DCPG)和Automatic Kernel Clustering with a Multi-Elitist Particle Swarm Optimization Algorithm (AKC-MEPSO)進行比較，以驗證該方法之準確性及有效性。驗證得知，本研究所提出AKC-BCO此一自動核心分群的方法在分群結果上表現較為穩定且優異。最後再將此方法應用於國內北部某教學醫院攝護腺癌症術後的存活狀況資料中，利用AKC-BCO自動核心分群方法找出最佳的分群結果，透過分群結果進行分析，提供醫院在攝護腺患者存活狀況上，給予適當的建議。

With the advancement of information technology, the methods of cluster analysis are continually proposed. Even though, most of the clustering methods still need to pre-determine the number of clusters. This study intends to propose a novel automatic kernel clustering technique- Automatic Kernel Clustering with Bee Colony Optimization Algorithm (AKC-BCO), which does not need to set the number of clusters in advance. By examining the data features, the proposed method is able to automatically cluster the data into a suitable number of groups. In addition, it also applies the kernel function to make the classification more correct than the conventional Euclidean distance.
This study employees four benchmark datasets, Iris, Wine, Glass and Vowel, to evaluate the accuracy and validity by comparing AKC-BCO with other three methods, Dynamic Clustering using Particle Swarm Optimization (DCPSO), Dynamic Clustering using Particle Swarm Optimization and Genetic Algorithm (DCPG) and Automatic Kernel Clustering with a Multi-Elitist Particle Swarm Optimization Algorithm (AKC-MEPSO). The experimental results indicate that AKC-BCO outperforms other methods in validity and stability. Finally, the proposed method is further applied to prostate cancer prognosis system. The results of AKC-BCO are still very promising. They can be used to provide appropriste suggestions for doctors regarding the patient survival situation.

中文部分
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蔡政軒，蜂群最佳化演算法於分群型資料探勘之應用與研究，佛光大學資訊應用研究所，碩士論文，宜蘭，2011。
莊惟丞，蜂群最佳化之實數問題求解研究，東海大學工業工程與經營資訊研究所，碩士論文，台中，2009。
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劉易昌，支援向量機於財務預測上之應用，靜宜大學資訊管理研究所，碩士論文，台中，2003。
余彥儒，圖書館書籍通閱移送之車輛途程問題－巨集啟發式演算法之應用，國立中央大學土木工程研究所，碩士論文，桃園，2010。

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