Determining the initial cluster centers 确定初始聚类中心点
In this paper,a new method is proposed to set initial cluster centers in K-means algorithm and EM algorithm,accelerates the convergence speed.
对K-均值算法和EM算法的初始聚类中心引进了改进算法,加快了算法的收敛速度。
参考来源 - 混合聚类彩色图像分割方法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
The new algorithm can obtain global optimal solutions through a new simple and efficient select rule of the initial cluster centers.
算法提出了一种简洁快速的初始聚类中心的选取规则，从而使获得的聚类结果为全局最优。
Experiment results show that proposed algorithm is less sensitive to noise and initial cluster centers in FCM method, and has better classification accuracy.
实验结果表明该方法较好地解决了FCM算法中的初始化和噪声敏感问题，具有较好的分类结果。
Given the number of desired clusters, randomly select that number of samples from the data set to serve as our initial test cluster centers.
理想的群集数量给定后，就可以随机地从数据集选择该数量的样例来充当我们初始测试群集中心。
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