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In this paper we propose a similarity-based clustering algorithm for handling LR-type fuzzy numbers. The proposed method does not need to specify a cluster number and initial values in which it is robust to initial values, cluster number, cluster shapes, noise and outliers for clustering LR-type fuzzy data. Numerical examples and real data demonstrate the effectiveness of the proposed clustering algorithm.
Ozdemir and Akarun (2001) proposed an intercluster separation (ICS) fuzzy clustering algorithm. The ICS algorithm is useful in combined quantization and dithering. However, there are two errors in the update equations for the ICS algorithm. This correspondence first points out these errors and gives their corrections. Since the parameters m, c, and γ are important factors in the performance of ICS,...
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