Abstract
A fuzzy clustering method for random fuzzy sets is proposed. The starting point is a p-value matrix with elements obtained by comparing the expected values of random fuzzy sets by means of a bootstrap test. As such, the p-value matrix can be viewed as a relational data matrix since the p-values represent a kind of similarity between random fuzzy sets. For this reason, in order to cluster random fuzzy sets, fuzzy clustering techniques for relational data can be applied. In this context, the so-called NE-FRC algorithm is considered. One of the most important advantages of the NE-FRC is that the relational data could not be derived from Euclidean distances. Some simulations are presented to show the behavior of the proposed procedure and two applications to real-life situations are also included.
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