分群是資料探勘中相當重要的一個技術，它是一種非監督式的學習方法。經過資料彼此間的相似度計算，再根據相似度將資料分配給各群，使得群內具有高相似度，跨群的資料則具有低相似度。在眾多的分群方法中，啟發式分群在近年來漸漸受到重視，啟發式分群指的是運用啟發式演算法或是啟發式的概念解決分群問題，以分群的技術類型來說，它屬於分割式分群的一種，但相較於著名的分割式分群演算法－k-means演算法，啟發式分群顯得更加穩定亦更具有效能。
　　一般的分群演算法在實作的時候，通常需要使用者給定群數這個先備知識，但多數的資料集空間維度過大，高維度使得資料難以觀察，想要給定群數這個資訊相對困難，因此，發展一個可以自動化決定群數的分群演算法，已經成為一個重要的議題。鑑於啟發式分群在分群問題的成功，開始有學者嘗試設計可以自動化分群的啟發式分群演算法，像是衍生自基因演算法(Genetic algorithm, GA)的GCUK(Genetic clustering for unknown K)，以及衍生自粒子群最佳化演算法(Particle swarm optimization, PSO)的MEPSO(Multi-Elitist PSO)。上述這兩種方法雖然可以成功的自動化分群，但卻有著效率不佳的問題，其原因有二：(1)編碼格式設計不佳(2)選用之啟發式演算法不適合分群問題或能力不足。
　　因此，本研究提出一個可以自動化決定群數的自動化分群系統(Automatic Clustering System, ACS)，此系統先使用群數搜尋演算法(Cluster Range Discovery algorithm, CRD)縮減欲搜尋的群數區間，再使用動態分群人造蜂群演算法(Dynamic Clustering Artificial Bee Colony algorithm, DCABC)進行自動化分群，DCABC加入了模範策略(Model Strategy)以克服既有人造蜂群演算法(Artificial Bee Colony algorithm, ABC)的缺點，並透過特別設計的編碼格式，使其可以在分群的時候同時達成決定群數和優化分群品質的功能。
　　最後，本研究將在實驗的部分驗證DCABC和ACS的效能。

Clustering is an important technology in data mining. It is an unsupervised learning method. Clustering algorithm partitions input data into each cluster through similarity measure, where the data within a cluster are similar and the data in different clusters are dissimilar. Among many clustering algorithms, Metaheuristic clustering algorithms becomes popular recently in solving clustering problems. In different types of clustering methods, Metaheuristic clustering is a partition clustering method. To compare with the most well-known partition clustering algorithm－k-means algorithm, Metaheuristic clustering seems to have more stable and better performances.
General clustering algorithms require users to provide prior knowledge of the dataset (e.g. the number of clusters). However, most datasets have high-dimensional, which makes the data hard to be observed, and thus the information (prior knowledge) is not easy to obtain. Therefore, it is important to develop a clustering algorithm which can automatically cluster the data and determine the number of clusters. With the success of Metaheuristic clustering, some researchers tend to design an automatic clustering algorithm with Metaheuristic method. For example, Genetic clustering for unknown K (GCUK) is based on Genetic algorithm (GA); and Multi-Elitist particle swarm optimization (MEPSO) is based on Particle swarm optimization (PSO). Though the two algorithms can successfully automatic clustering, their performances are not very good. Here are two possible reasons: (1) Encoding design is improper (2) The selected Metaheuristic algorithm does not suit the clustering problem or it lacks capability.
This study proposes an Automatic Clustering System(ACS), which can automatically determine the number of clusters. First, ACS uses a Cluster Range Discovery algorithm(CRD) to reduce the search range of cluster number. Then, ACS uses Dynamic Clustering Artificial Bee Colony algorithm(DCABC) to complete the automatic clustering. DCABC adopts the Model Strategy to overcome the drawback of original Artificial bee colony algorithm (ABC). DCABC also designs a brand-new encoding format. Combining this encoding format, DCABC can cluster the data and find the number of clusters simultaneously. Finally, the performance of the DCABC was examined in the study.

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