关键词: 密度峰, 自然最近邻居, 局部密度, 稀疏区域, 类簇间相似度

Abstract: Aiming at the problem that the existing density-based clustering algorithm is sensitive to parameters and the clustering result of aspheric data and complex manifold data is bad, a new clustering algorithm based on density peak is proposed. The algorithm first determines the local density of data based on the natural nearest neighbor, and then determines the clustering center based on which density peaks have the highest local density and are divided by sparse regions. Finally, a new concept of similarity between clusters is proposed to solve complex manifold problems. In the experiment, the performance of this algorithm is better than that of DPC (clustering by fast search and find of density peaks), DBSCAN (density-based spatial clustering of applications with noise) and K-means in synthetic and actual data sets, and the advantages of aspheric data and complex manifold data are particularly superior.

Key words: density peak, natural nearest neighbor, local density, sparse regions, similarity between clusters