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- Title
融合流形距离与标签传播的改进密度峰值聚类.
- Authors
陈超泉; 王佳明; 谢晓兰
- Abstract
The Euclidean distance is dependent on the local density in the density peaking algorithm. The algorithm is not very effective in dealing with datasets with high-dimensional data and the presence of class clusters with inhomogeneous density. To address these problems, improved density peak clustering combining manifold distance and label popagation (DPC-ML) was proposed. The distance metric in DPC-ML was defined by the manifold distance and forms the manifold distance matrix. And a local density was redefined to fuse the manifold distance with the local density, so that the local density reflects certain local distance information. The experimental data show that the algorithm has good performance in dealing with different shapes and uneven density of class clusters. Moreover, the selection of class cluster centroids was found to be more discriminative in terms of the redefined local density in decision maps drawn by using the DPC-ML algorithm on different artificial data sets. Since a new parameter neighborhood points is introduced, the effect of neighborhood points on clustering results is also explored, and it is found that the clustering index works best when it just becomes a connected graph, further demonstrating that the performance of the clustering results can be improved by the manifold distance.
- Publication
Science Technology & Engineering, 2022, Vol 22, Issue 10, p4011
- ISSN
1671-1815
- Publication type
Article