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运用遗传优化算法优化模糊C均值聚类，通过全局自适应寻优，寻找出更为精确的模糊聚类中心-Using genetic optimization algorithm to optimize the fuzzy C-means clustering, global adaptive optimization to find a more precise fuzzy clustering center
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遗传算法 优化K均值算法 实现两种算法的优势互补 -Genetic Algorithm K-means algorithm to achieve complementary advantages of the two algorithms
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matlab code for Genetic k means algorithm
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matlab编写的遗传算法优化的模糊C均值聚类算法-genetic algorithm optimize Fuzzy C means algorithm
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该算法引入遗传算法对模糊c均值算法进行改进，并在iris数据集中进行实验验证，得到很高的正确率。-The algorithm genetic algorithm fuzzy c-means algorithm is improved, and focus on experiments in the iris data to obtain a high accuracy.
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遗传算法无功优化，基于K均值的PSO聚类算法,完整的图像处理课设，包含所有源代码，汽车图像。- Genetic algorithm based reactive power optimization, K-means clustering algorithm based on the PSO, Complete class-based image processing, contains all of the source code, auto image.
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使用k-means算法对图像进行分割，并利用遗传算法对k-means算法加以改进(The k-means algorithm is used for the segment of images, and the genetic algorithm is used to improve the k-means algorithm)
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遗传算法改进的模糊C-均值聚类MATLAB源码.模糊C-均值算法容易收敛于局部极小点，为了克服该缺点，将遗传算法应用于模糊C-均值算法(FCM)的优化计算中，由遗传算法得到初始聚类中心，再使用标准的模糊C-均值聚类算法得到最优分类结果。(Improved genetic algorithm and fuzzy C- means clustering MATLAB source. The fuzzy C- means algorithm is easy to converge to local m
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