Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzy c-Medoids (FCMdd) concept, in which Fuzzy c-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.
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机译：已经开发了关系模糊聚类，用于提取关系数据的内在集群结构，并扩展到基于模糊C-METOIDS（FCMDD）概念的线性模糊聚类模型，其中执行了类似迭代算法的模糊C-MATCLIOM-（FCM-）通过使用两个代表性的麦细测数定义线性簇原型，每个线路原型。在本文中，进一步修改了FCMDD型线性聚类模型，以处理包括缺失值的不完整数据，并且比较了几种估算方法的适用性。 In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.
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