Most clustering algorithms build disjoint clusters. However, clusters might be overlapped because documents may belong to two or more categories in the real world. For example, a paper discussing the Apple Watch may be categorized into either 3C, Fashion, or even Clothing and Shoes. Therefore, overlapping clustering algorithms have been studied such that a resource can be assigned to one or more clusters. Formal Concept Analysis (FCA), which has many practical applications in information science, has been used in disjoin clustering, but has not been studied in overlapping clustering. To make overlapping clustering possible by using FCA, we propose an approach, including two types of transformation. From the experimental results, it shows that the proposed fuzzy overlapping clustering performed more efficiently than existing overlapping clustering methods. The positive results confirm the feasibility of the proposed scheme used in overlapping clustering. Also, it can be used in applications such as recommendation systems.

中文翻译：

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基于形式概念分析的模糊重叠聚类多文档分类

大多数聚类算法构建不相交的聚类。但是，集群可能会重叠，因为文档可能属于现实世界中的两个或多个类别。例如，一篇讨论 Apple Watch 的论文可能被归类为 3C、时尚，甚至服装和鞋类。因此，已经研究了重叠聚类算法，以便可以将资源分配给一个或多个聚类。形式概念分析（FCA）在信息科学中有许多实际应用，已用于分离聚类，但尚未研究重叠聚类。为了使用 FCA 使重叠聚类成为可能，我们提出了一种方法，包括两种类型的转换。从实验结果来看，它表明，所提出的模糊重叠聚类比现有的重叠聚类方法更有效。积极的结果证实了所提出的方案在重叠聚类中的可行性。此外，它还可以用于推荐系统等应用程序。