关键词: 密度峰值, 聚类, K近邻, 互逆近邻, 局部密度, 分配策略

Abstract:

The density peaks clustering algorithm tends to select the density peaks in the spherical distribution data, while the manifold data are mostly non spherical distribution, resulting in the inability to accurately find the cluster centers. The allocation strategy of the algorithm gives priority to the chain allocation of samples near the cluster centers, while a large number of samples of manifold data are far away from the cluster centers, resulting in the wrong allocation of samples that should belong to the same cluster. Therefore, this paper proposes a density peaks clustering algorithm based on geodesic distance and cosine mutual reverse nearest neighbors for manifold datasets. Combining K-nearest neighbors with geodesic distance and redefining local density, highlighting the difference between density peaks and non density peaks, accurately find the cluster centers; combining the mutual reverse nearest neighbors and cosine similarity, the sample similarity matrix based on cosine mutual reverse nearest neighbors is obtained, which can accurately allocate samples for manifold clusters. The experimental results show that the algorithm can effectively find the geometry structure of manifold datasets, and has excellent clustering effect on real datasets and picture datasets.

Key words: density peaks, clustering, K-nearest neighbors, mutual reverse nearest neighbors, local density, allocation strategy