The traditional FCM clustering algorithm relies on the initial clustering center.According to the characteristics of randomly selecting the initial clustering center,it is easy to cause the objective function to fall into the local optimal solution,and the convergence speed of large-scale data sets is slow.This paper proposes a density based canopy algorithm to improve the original FCM algorithm,so as to automatically select the initial clustering center,and take the selected clustering center as the input of FCM algorithm,in order to speed up the convergence of the algorithm and reduce the inaccurate accuracy of the classification re-sults caused by random selection.
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