中文摘要：

现有的K-Means聚类算法均直接作用于多维数据集上，因此，当数据集基数和聚类属性个数较大时，这些聚类算法的效率极其低下．为此，文中提出一种基于正规格结构的有效聚类方法（KMCRG）.KMCRG算法以单元格为处理对象来有效完成K-Means聚类工作．特别，该算法使用格加权迭代的策略来有效返回最终的K个类．实验结果表明，KMCRG算法在不损失聚类精度的基础上能够快速返回聚类结果．

英文摘要：

The existing K-Means clustering methods directly act on multidimensional datasets. Hence, these methods are extremely inefficient as the cardinality of input data and the number of clustering attributes increase. Motivated by the above fact, in this paper, an efficient approach for K-Means clustering based on the structure of regular grid, called KMCRG （ K-Means Clustering based on Regular Grid）, is proposed. This method effectively implements K-Means clustering by taking cell as handling object. Especially, this method uses the tactics of grid weighted iteration to effectively gain the final K classes. The experiment results show that the algorithm can quickly gain the clustering results without losing clustering precision.