计算机工程与应用2023，Vol.59Issue(24)：78-87,10.DOI:10.3778/j.issn.1002-8331.2302-0037
k近邻密度支配域代表团密度峰值聚类算法
k-NN Density Dominator Component Delegations Based Density Peaks Clustering
摘要
Abstract
DPC(clustering by fast search and find of density peaks)is inefficient in processing large-scale clustering.k(lower case)-NN density dominator component skill can improve such shortcoming.However,representative data points in such skill could have poor ability on representation,which leads to lower clustering quality.The delegation sampling strategy can be used as an improvement on the above issue.The resulting new algorithm not only inherits the efficient characteristics of density dominator component acceleration skill,but also ensures the quality of clustering.This algo-rithm first constructs k-nearest neighbor graph.Then,kernel density is estimated and density dominator component is built.Thirdly,each density dominator component is sampled from its high and low density area,and similarity is computed between each dominator via delegations'nearest neighbor relationship.Finally,DPC algorithm is conducted with each domain as the data point.The experiments show that the introduction of delegations strategy can improve the performance of the original DPC,and the clustering results are better than some other density clustering algorithms.关键词
密度峰值聚类/k近邻图/密度支配域/代表团策略/大规模聚类Key words
density peak clustering/k-nearest neighbor graph/density dominator component/delegations strategy/large-scale clustering分类
信息技术与安全科学引用本文复制引用
吕鸿章,杨易扬,杨戈平,巩志国..k近邻密度支配域代表团密度峰值聚类算法[J].计算机工程与应用,2023,59(24):78-87,10.基金项目
国家自然科学基金(61603101,61876043,61976052) （61603101,61876043,61976052）
广东省自然科学基金(2021A1515011941) （2021A1515011941）
国家优秀青年科学基金(62122022) （62122022）
国家重点研发计划(2021ZD0111501). （2021ZD0111501）
