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Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering

机译：通过改进的支持向量聚类学习模糊集的隶属函数

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摘要

We propose an algorithm for inferring membership functions of fuzzy sets by exploiting a procedure originated in the realm of support vector clustering. The available data set consists of points associated with a quantitative evaluation of their membership degree to a fuzzy set. The data are clustered in order to form a core gathering all points definitely belonging to the set. This core is subsequently refined into a membership function. The method is analyzed and applied to several real-world data sets.
机译：我们提出了一种算法，该算法通过利用支持向量聚类领域中起源的过程来推断模糊集的隶属函数。可用的数据集由与模糊度隶属度的定量评估相关的点组成。数据被聚类以形成一个核心，该核心收集肯定属于该集合的所有点。随后将此核心完善为成员函数。分析了该方法并将其应用于多个实际数据集。

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