应用硬聚类算法及模糊聚类算法解决数据分类问题时，在迭代过程中容易陷入局部极小.本文提出了一种基于免疫遗传算法的模糊C均值聚类算法，克服了现有算法的缺点.将本文算法应用于葡萄酒和鸢尾花数据集，得出结果说明其有效性.
Abstract
When the hard clustering algorithm and fuzzy clustering algorithm are used to solve the problem of data classification, it is easy to fall into the local minimum in the iterative process. In this paper, a fuzzy C-means clustering algorithm based on immune genetic algorithm is proposed, which overcomes the shortcomings of the existing algorithms. This algorithm is applied to the dataset of wine and iris, and the results show its effectiveness.
关键词
模糊C均值 /
免疫遗传算法 /
葡萄酒 /
鸢尾花
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Key words
Fuzzy c-means /
Immune genetic algorithm /
Wine /
Iris
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