中文摘要：

规模约束可有效改善聚类算法的性能,但是各类规模约束后所含实例对象数量不一致将降低聚类算法的性能.采用一种新的模式对各类进行了规模约束,并转化为线性规划问题进行求解.UCI标准数据集上的实验结果表明本算法与随机模式相比具有更好的聚类精度,即使当规模约束适当放宽后,聚类性能也可得到明显提升.提出的方法能够有效地提高聚类的准确性.

英文摘要：

Size constraints can improve the clustering performance of clustering methods.However the differences in the size of clusters,i.e.the number of instances contained in each cluster will decrease the clustering performance.This paper introduces a new scheme of size constraints on size of each cluster and transforms them into linear programming optimization.Experiments results on UCI benchmark datasets show that the new method outperforms the random scheme.The clustering performance can be increased even when the size constraints are relaxed to some extent.The new algorithm can increase the clustering accuracy efficiently.