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。
Therefore, RPCL is utilized to converge some of initial centers to actual centers of original color image and image is segmented by these learned cluster centers.
因此，本文采用RPCL算法，对这些组合的聚类中心颜色进行学习来确定实际的颜色类数目以及聚类中心，并用学习后的聚类中心对原图像进行聚类分割。
Therefore, RPCL is utilized to converge some of initial centers to actual centers of original color image and image is segmented by these learned cluster centers.
因此，本文采用RPCL算法，对这些组合的聚类中心颜色进行学习来确定实际的颜色类数目以及聚类中心，并用学习后的聚类中心对原图像进行聚类分割。
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