但是，组合的聚类中心数目会多于实际的聚类数目，造成过度分割。
But the number of these combined clusters may be larger than that of the actual clusters, which may result in the over-segmentation.
同时，算法可以在训练过程中通过有效性函数自适应地确定最佳聚类数目。
Further more, this method can determine the best clustering number using the validity function in training progress.
对于许多聚类算法，决定合适的聚类数目至关重要，这称为聚类有效性问题。
For many clustering algorithms, it is very important to determine an appropriate number of clusters, which is called cluster validity problem.
本算法改进传统算法对噪声点敏感的缺点，并解决了传统超平面聚类初始需要指定聚类数目的不足。
The robust K-plane clustering algorithm can reduce the sensitivity of the traditional K-plane clustering algorithm to noises and the predefined number of clustering is not necessary.
改进后的模糊C-均值聚类算法具有更好的鲁棒性，且放松了隶属度条件，使得最终聚类结果对预先确定的聚类数目不敏感。
The improved fuzzy C-means clustering algorithm has better robustness and makes the cluster results insensitive to the predefined cluster number.
该算法通过引入聚类有效性函数，实现了最优特征数目的自动确定。
The optimal feature number is decided automatically by the introduced cluster validity function.
以图像的布朗维数为纹理特征对编码中的图像块进行聚类和排序，实现了对每个值域块所需比较定义域块数目的精确控制。
Taking the Brownian dimension as their texture feature, image blocks were clustered and sorted, to control the number of domain blocks to be compared with each range block in coding.
在模糊聚类算法的基础上，提出了一个衡量聚类有效性的函数，以确定模糊规则的数目。
A function for measuring clustering validity based on the fuzzy clustering algorithm is defined with which the number of fuzzy rules can be determined.
文章采用分层聚类算法，定义了新的准则函数，同时解决了确定星座点实际位置和星座点数目的问题。
In this research hierarchical clustering algorithm is used with a newly defined criterion function, and it can cluster data without known cluster number.
本文提出一种基于两层结构的彩色图像聚类系统，该系统能够自动判定颜色数目和聚类中心。
In this paper, proposed a color image clustering segmentation system based on a two-level structure, which allow the number of color and cluster centers to be determined automatically.
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。
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.
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