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In allusion to the disadvantages that fuzzy c-means algorithm is sensitive to noise and possibilistic c-means is easy to generate superposition cluster center, a novel algorithm (FPCM) which simultaneously produces both memberships and possibilities was proposed in 1997. However, FPCM still uses a norm-induced distance, as a consequence, its performance on the noisy data is not strong enough. In this...
In allusion to the disadvantages that fuzzy c-means algorithm is sensitivity to noise and possibilistic c-means is easy to generate superposition cluster center, interval attributes description based FCM clustering algorithm is proposed in this paper. Firstly, an interval attributes description model of noisy data is presented. Then a clustering algorithm of interval attributes data based on two ends...
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