《计算机应用研究》|Application Research of Computers

基于密度峰值优化的谱聚类算法

Spectral clustering based on density peak value optimization

免费全文下载 (已被下载 次)  
获取PDF全文
作者 薛丽霞,孙伟,汪荣贵,杨娟,胡敏
机构 合肥工业大学 计算机与信息学院,合肥 230009
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2019)07-006-1948-03
DOI 10.19734/j.issn.1001-3695.2018.01.0019
摘要 针对经典谱聚类算法无法自适应确定聚类数目,以及在处理大数据量的聚类问题时效率不高的问题,提出了一种基于密度峰值优化的谱聚类算法。该方法首先计算数据对象的局部密度,以及每个数据对象与其他数据对象的最小距离,并依据一定的规则自适应产生初始聚类中心,确定聚类数目;然后使用Nystrm抽样来降低特征分解的计算复杂度,以达到提高谱聚类算法的效率。实验结果表明,该方法能够准确地得到聚类数目,并且有效提高了聚类的准确率和效率。
关键词 谱聚类; 密度峰值; 密度聚类; 自适应; Nystrm抽样
基金项目 国家自然科学基金资助项目(61672202)
本文URL http://www.arocmag.com/article/01-2019-07-006.html
英文标题 Spectral clustering based on density peak value optimization
作者英文名 Xue Lixia, Sun Wei, Wang Ronggui, Yang Juan, Hu Min
机构英文名 School of Computer Science & Information Engineering,Hefei University of Technology,Hefei 230009,China
英文摘要 To deal with the problem that classical spectral clustering algorithms are unable to determine the number of clusters automatically, and low efficiency in processing large amount of data with, this paper proposed a spectral clustering algorithm based on the optimization of density peak value. The method firstly calculated the local density of data object and the minimum distance between each data object and other data objects. It generated adaptive clustering algorithm to determine the number of clusters and to optimize the number of clusters according to certain rules. Secondly, adopting Nystrm sampling could reduce the time complexity of characteristic decomposition and improved the efficiency of the algorithm. The experimental results show that this method can accurately obtain the number of clusters and effectively improve the accuracy and efficiency of clustering effectively.
英文关键词 spectral clustering; density peak; density clustering; adaptive; Nystrm sampling
参考文献 查看稿件参考文献
 
收稿日期 2018/1/9
修回日期 2018/3/5
页码 1948-1950,1983
中图分类号 TP301.6
文献标志码 A