Improved Density Peaks Clustering Based on Shared-Neighbors of Local Cores for Manifold Data Sets
Improved Density Peaks Clustering Based on Shared-Neighbors of Local Cores for Manifold Data Sets
复制标题
流形数据集基于局部核共享邻居的改进密度峰聚类
DOI：
10.1109/access.2019.2948422
复制
发表时间：
2019-10
期刊：
影响因子：
3.9
通讯作者：
Liu Huijun
中科院分区：
文献类型：
--
作者：
Cheng Dongdong;Huang Jinlong;Zhang Sulan;Liu Huijun
A novel clustering algorithm by fast search and find of density peaks (DP) was proposed in Science, 2014. It has attracted much attention from researchers. It can easily select clusters centers with decision graph. However, it cannot be used to cluster manifold data sets as the existing distance measurement is not suitable to evaluate the dissimilarity between objects on manifold structure. Some researchers use graph-based distance to measure the dissimilarity between objects on manifold clusters, but computing the graph-based distance on the original data set is time consuming. An improved density peaks clustering algorithm based on shared-neighbors between local cores, SLORE-DP, is proposed in this paper. First, it finds local cores to represent the data set and redefines the graph-based distance between local cores with shared-neighbors-based distance. Then natural neighbor-based density and the new defined graph-based distance are used to construct decision graph on local cores and DP algorithm is employed to cluster local cores. Finally, the remaining points are assigned to the same cluster as their local cores belong to. Since we use the new defined graph-based distance to estimate the dissimilarity between local cores, SLORE-DP can be used to cluster manifold data sets and at the same time it only calculates the shortest path between local cores, which greatly reduces the running time of the algorithm. We do experiments on several synthetic data sets containing manifold clusters and several real data sets from UCI. The results show that SLORE-DP is more effective and efficient than other algorithms when clustering manifold data sets.
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