Since its early formulations, co-clustering has gained popularity and interest both within and outside the machine learning community as a powerful learning paradigm for clustering high-dimensional data with good explainability properties. The simultaneous partitioning of all the modes of the input data tensors (rows and columns in a data matrix) is both a method for improving clustering on one mode while performing dimensionality reduction on the other mode(s), and a tool for providing an actionable interpretation of the clusters in the main mode as summaries of the features in each other mode(s). Hence, it is useful in many complex decision systems and data science applications. In this paper, we survey the the co-clustering literature by reviewing the main co-clustering methods, with a special focus on the work done in the last twenty-five years. We identify, describe and compare the main algorithmic categories, and provide a practical characterization with respect to similar unsupervised techniques. Additionally, we also try to explain why it is still a powerful tool despite the apparent recent decreasing interest shown by the machine learning community. To this purpose, we review the most recent trends in co-clustering research and outline the open problems and promising future research perspectives.

中文翻译：

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共聚类：主要方法、近期趋势和开放问题综述


自早期表述以来，协聚在机器学习社区内外都获得了普及和兴趣，作为一种强大的学习范式，用于对具有良好可解释性特性的高维数据进行聚类。同时对输入数据张量（数据矩阵中的行和列）的所有模式进行分区既是一种在一种模式下改进聚类，同时在其他模式下执行降维的方法，也是一种在主模式下提供聚类的可操作解释的工具，作为其他模式下特征的摘要。因此，它在许多复杂的决策系统和数据科学应用程序中很有用。在本文中，我们通过回顾主要的共聚类方法来调查共聚类文献，特别关注过去 25 年所做的工作。我们识别、描述和比较主要的算法类别，并提供有关类似无监督技术的实用特征。此外，我们还试图解释为什么尽管机器学习社区最近表现出的兴趣明显下降，但它仍然是一个强大的工具。为此，我们回顾了共聚研究的最新趋势，并概述了开放的问题和有希望的未来研究前景。