A probability based defuzzification method for fuzzy cluster partition
نویسندگان
چکیده
Fuzzy C-Means (FCM) is an unsupervised clustering method that has been used extensively in data analysis and image segmentation. The defuzzification of the fuzzy partition of FCM is usually done using the maximum membership degree principle which may not be appropriate for some real-world applications. In this paper, we present a new algorithm that generates a probabilistic model of the fuzzy partition and applies the model to classification of data objects. We show that our method outperforms four popular defuzzification methods on uniform and nonuniform artificial datasets as well as on real datasets. Availability: The test datasets and the method software are available online at http://ouray.ucdenver.edu/~tnle/fzpbd.
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