Description: Because K-means clustering classification depend on the training sample selection,great easy to fall into local optimum, in this paper , using Simulated Annealing algorithm to optimize K-means cluster analysis to obtain the global best optimal solution of the classification of 20 the new program. And multi-band image as an example to verify the analysis results, showing that the method is feasible, convergence results are better than K-means clustering algorithm, classification accuracy compared to traditional K-means algorithm is higher
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基于模拟退火的k均值算法.pdf
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