基于密度峰值优化的谱聚类算法

o deal with the problem that classical spectral clustering algorithms are unable to determine the number of clusters automatically, and low efficiency in processing large amount of data with. This paper proposes a spectral clustering algorithm based on the optimization of density peak value. The method firstly calculates the local density of data object and the minimum distance between each data object and other data objects. Adaptive clustering algorithm is generated to determine the number of clusters and to optimize the number of clusters according to certain rules. Secondly, adopting Nystr sampling can reduce the time complexity of characteristic decomposition and improve the efficiency of the algorithm. The experimental results show that this method can accurately obtain the number of clusters and effectively improve the accuracy and efficiency of clustering effectively.