分群是根據資料點之間的相似性將整個資料集劃分為一個分群結果的過程。現今已經有許多分群的方法被提出，但由於這些方法各別的性質，不同的方法通常會產生不同的分群結果。為了將不同的分群結果結合成一個更好且更穩定的分群結果，分群集成因此被提出。然而，有些分群集成方法要求使用者事先指定參數，同時分群的結果通常受到這些參數設置的嚴重影響。在這篇論文中，我們提出了一個聚類集成方法稱為全局投票分群集成演算法，其中不需要任何手動參數，包括聚類的數量。在此方法包含三個階段，在創建階段，將提取片段以進一步建立適當的候選群。在投票階段，投票過程被設計來使候選群演變成最終候選群。最後，在指定階段，確保每個資料點僅分配給一個候選群。經過各種真實資料集的實驗結果能夠證實，我們提出的方法的確能夠擁有更佳的精準度。

Clustering is the process of partitioning a dataset into clusters based on the similar-ity between data points. Many clustering methods have been proposed, but different methods usually generate different clustering results because of various properties of these methods. To combine different clustering results into a better and more robust clustering result, clustering ensemble has been proposed. However, some methods re-quire users to specify parameters, and results are generally affected by settings of pa-rameters severely. In this work, a novel clustering ensemble method called Global Voting Clustering Ensemble (GVCE) is proposed, where no parameter is required, in-cluding the number of clusters. There are three satges in GVCE, in the creating stage, fragments will be extracted to further create appropriate candidate clusters. Then, in the voting stage, a voting process is designed to allow candidate clusters to evolve into final candidate clusters containing all data points. Finally, an assigning stage is devised to make sure that each data point is assigned to only one cluster. Experimental results on a variety of real-world datasets verified that our proposed method is better than state-of-the-art clustering ensemble methods.

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