摘要
针对k-medoids算法对初始聚类中心敏感,聚类精度较低及收敛速度缓慢的缺点,提出一种基于密度初始化、密度迭代的搜索策略和准则函数优化的方法。该算法初始化是在高密度区域内选择k个相对距离较远的样本作为聚类初始中心,有效定位聚类的最终中心点;在k个与初始中心点密度相近的区域内进行中心点替换,以减少候选点的搜索范围;采用类间距和类内距加权的均衡化准则函数,提高聚类精度。实验结果表明,相对于传统的k-mediods算法及某些改进算法,该算法可以提高聚类质量,有效缩短聚类时间。
For the disadvantages that sensitivity to centers initialization, lower clustering accuracy and slow convergent speed of k-medoids algorithm, a novel k-medoids algorithm based on density initialization, density of iterative search strategy and optimi-zation criterion function is proposed. The Initialization of the algorithm is that, it chooses k cluster centers in the high-density area which are far apart, effectively positioning of the final cluster center. To replace the centers are in the ranges which are proximity to the k-initial centers, to reduce the scope of the search candidate point. Criterion function of equalization based on class density and within-class density weighted is adopted to improve the clustering precision. Experimental results show that this algorithm can improve the clustering quality, shorten the clustering time compared with traditional k-medoids algorithms or other improved algorithms.
出处
《计算机工程与应用》
CSCD
2013年第19期153-157,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.11171095,No.10871031)
湖南省自然科学衡阳联合基金(No.10JJ8008)
湖南省教育厅重点项目(No.10A015)
湖南省科技计划项目(No.2011FJ3051)
