Abstract: Traditional clustering ensemble combines all of the available clustering partitions to get the final clustering result. But in supervised classification area, it has been known that selective classifier ensembles can always achieve better solutions. Following the selective classifier ensembles, the question of clustering ensemble is defined as clustering ensemble selection. The paper introduces the concept of clustering ensemble selection, gives the survey of clustering ensemble selection algorithms and discusses the future directions of clustering ensemble selection.

Key words: clustering ensemble, clustering ensemble selection, selection strategy, consensus function

摘要： 传统的聚类融合方法通常是将所有产生的聚类成员融合以获得最终的聚类结果。在监督学习中，选择分类融合方法会获得更好的结果，从选择分类融合中得到启示，在聚类融合中应用这种方法被定义为选择性聚类融合。对选择性聚类融合关键技术进行了综述，讨论了未来的研究方向。

关键词: 聚类融合, 选择性聚类融合, 选择策略, 融合函数