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.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2010年第4期516-521,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60903032
70771077)
教育部博士点基金项目(No.20090072120056)
国家863计划项目(No.2008AA04Z106)资助
关键词
K-MEANS聚类
正规格结构
性能评估
K-Means Clustering, Regular Grid Structure, Performance Evaluation
