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Multi-sample test-based clustering for fuzzy random variables
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DOI：10.1016/j.ijar.2009.01.003.png)
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Abstract
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A clustering method to group independent fuzzy random variables observed on a sample by focusing on their expected values is developed. The procedure is iterative and based on the p-value of a multi-sample bootstrap test. Thus, it simultaneously takes into account fuzziness and stochastic variability. Moreover, an objective stopping criterion leading to statistically equal groups different from each other is provided. Some simulations to show the performance of this inferential approach are included. The results are illustrated by means of a case study. (C) 2009 Elsevier Inc. All rights reserved.
Keywords:
Clustering
Fuzzy random variable
Multi-sample test
Bootstrap hypothesis testing
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