In the realm of information fusion, clustering stands out as a common subject and is extensively applied across various fields. Evidential clustering, an increasingly popular method in the soft clustering family, derives its strength from the theory of belief functions, which enables it to effectively characterize the uncertainty and imprecision of data distributions. This survey provides a comprehensive overview of evidential clustering, detailing its theoretical foundations, methodologies, and applications. Specifically, we start by briefly recalling the theory of belief functions with its transformations into other uncertainty reasoning theories. Then, we introduce the concepts of soft data, partitions, and methods with an emphasis on data and partitioning within the theory of belief functions. Subsequently, we summarize the advancements and quantitative evaluations of existing evidential clustering methods and provide a roadmap to help in selecting an appropriate method based on specific application needs. Finally, we identify the major challenges faced in the development and application of evidential clustering, pointing out promising avenues for future research, including theoretical limitations, applicable datasets, and application domains. The survey offers a structured understanding of existing evidential clustering methods, highlighting their theoretical underpinnings, practical implementations, and future research directions. It serves as a valuable resource for researchers seeking to deepen their understanding of evidential clustering.

中文翻译：

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证据聚类调查：定义、方法和应用


在信息融合领域，聚类是一个常见的主题，并广泛应用于各个领域。证据聚类是软聚类家族中一种越来越流行的方法，其优势来源于置信函数理论，这使其能够有效地描述数据分布的不确定性和不精确性。本调查全面概述了证据聚类，详细介绍了其理论基础、方法和应用。具体来说，我们首先简要回顾一下信念函数理论及其向其他不确定性推理理论的转变。然后，我们介绍了软数据、分区和方法的概念，重点是信念函数理论中的数据和分区。随后，我们总结了现有证据聚类方法的进展和定量评估，并提供了一个路线图，以帮助根据特定的应用需求选择合适的方法。最后，我们确定了证据聚类的开发和应用面临的主要挑战，指出了未来研究的有希望的途径，包括理论局限性、适用的数据集和应用领域。该调查提供了对现有证据聚类方法的结构化理解，突出了它们的理论基础、实际实施和未来的研究方向。对于寻求加深对证据聚类的理解的研究人员来说，它是宝贵的资源。