文献类型：期刊文献

英文题名：Fast Searching Density Peak Clustering Algorithm Based on Shared Nearest Neighbor and Adaptive Clustering Center

作者：Lv, Yi[1];Liu, Mandan[1];Xiang, Yue[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2020

卷号：12

期号：12

外文期刊名：SYMMETRY-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000602314500001)】；

基金：This research was funded by Fundamental Research Funds for the Central Universities, grant number 222201917006

语种：英文

外文关键词：clustering by fast search and find density peaks; shared-nearest neighbor; adaptive clustering center; knee point

摘要：The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms always need prior knowledge, we proposed a fast searching density peak clustering algorithm based on the shared nearest neighbor and adaptive clustering center (DPC-SNNACC) algorithm. It can automatically ascertain the number of knee points in the decision graph according to the characteristics of different datasets, and further determine the number of clustering centers without human intervention. First, an improved calculation method of local density based on the symmetric distance matrix was proposed. Then, the position of knee point was obtained by calculating the change in the difference between decision values. Finally, the experimental and comparative evaluation of several datasets from diverse domains established the viability of the DPC-SNNACC algorithm.