计算机工程与科学2017，Vol.39Issue(5)：964-970,7.DOI:10.3969/j.issn.1007-130X.2017.05.022
基于网格的快速搜寻密度峰值的聚类算法优化研究
Optimization of grid based clustering by fast search and find of density peaks
摘要
Abstract
The CFSFDP is a clustering algorithm based on density peaks,which can cluster arbitrary shape data sets,and has the advantages of fast clustering and simple realization.However,the global density threshold dc,which can lead to the decrease of clustering quality,is specified without the consideration of spatial distribution of the data.Moreover,the data sets with multi-density peaks cannot be clustered accurately.To resolve the above shortcomings,we propose an optimized CFSFDP algorithm based on grid (GbCFSFDP).To avoid the using of global dc,the algorithm divides the data sets into smaller partitions by using the grid partitioning method and performs local clustering on them.Then the GbCFSFDP merges the sub classes.Data sets,which are unevenly distributed and have multi-density peaks,are correctly classified.Simulation experiments of two typical data sets show that the GbCFSFDP algorithm is more accurate than the CFSFDP.关键词
聚类/密度阈值/网格分区/类合并Key words
clustering/density threshold/grid partition/merging clusters分类
信息技术与安全科学引用本文复制引用
孙昊,张明新,戴娇,尚赵伟..基于网格的快速搜寻密度峰值的聚类算法优化研究[J].计算机工程与科学,2017,39(5):964-970,7.基金项目
国家自然科学基金(61173130) （61173130）
