Journals
  Publication Years
  Keywords
Search within results Open Search
Please wait a minute...
For Selected: Toggle Thumbnails
Parameter-free clustering algorithm based on Laplace centrality and density peaks
QIU Baozhi, CHENG Luan
Journal of Computer Applications    2018, 38 (9): 2511-2514.   DOI: 10.11772/j.issn.1001-9081.2018010177
Abstract639)      PDF (780KB)(455)       Save
In order to solve the problem of selecting center manually in a clustering algorithm, a Parameter-free Clustering Algorithm based on Laplace centrality and density peaks (ALPC) was proposed. Laplace centrality was used to measure the centrality of objects, and a normal distribution probability statistical method was used to determine clustering centers. The problem that clustering algorithms rely on empirical parameters and manually determine cluster centers was solved by the proposed algorithm. Each object was assigned to the corresponding cluster center according to the distance between the object and the center. The experimental results on synthetic data sets and UCI data sets show that the new algorithm can not only automatically determine cluster centers without any priori parameters, but also get better results with higher accuracy compared with the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm, Clustering by fast search and find of Density Peaks (DPC) algorithm and Laplace centrality Peaks Clustering (LPC) algorithm.
Reference | Related Articles | Metrics