Clustering is a fundamental data exploration technique to discover hidden grouping structure of data. With the proliferation of big data, and the increase of volume and variety, the complexity of data multiplicity is increasing as well. Traditional clustering methods can provide only a single clustering result, which restricts data exploration to one single possible partition. In contrast, multiple clustering can simultaneously or sequentially uncover multiple non-redundant and distinct clustering solutions, which can reveal multiple interesting hidden structures of the data from different perspectives. For these reasons, multiple clustering has become a popular and promising field of study. In this survey, we have conducted a systematic review of the existing multiple clustering methods. Specifically, we categorize existing approaches according to four different perspectives (i.e., multiple clustering in the original space, in subspaces and on multi-view data, and multiple co-clustering). We summarize the key ideas underlying the techniques and their objective functions, and discuss the advantages and disadvantages of each. In addition, we built a repository of multiple clustering resources (i.e., benchmark datasets and codes). Finally, we discuss the key open issues for future investigation.

中文翻译：

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多重聚类：最新进展和观点

聚类是一种基本的数据探索技术，用于发现数据的隐藏分组结构。随着大数据的激增、数量和种类的增加，数据多样性的复杂性也随之增加。传统的聚类方法只能提供单个聚类结果，这将数据探索限制在一个可能的分区。相比之下，多重聚类可以同时或顺序地揭示多个非冗余且不同的聚类解决方案，从而可以从不同的角度揭示数据的多个有趣的隐藏结构。由于这些原因，多重聚类已成为一个流行且有前途的研究领域。在本次调查中，我们对现有的多种聚类方法进行了系统回顾。具体来说，我们根据四个不同的角度对现有方法进行分类（即原始空间中的多重聚类、子空间中的多重聚类、多视图数据上的多重聚类以及多重联合聚类）。我们总结了这些技术及其目标函数背后的关键思想，并讨论了每种技术的优缺点。此外，我们还构建了多个集群资源（即基准数据集和代码）的存储库。最后，我们讨论了未来研究的关键开放问题。