%A GUO Peng, LI Ren-fa, HU Hui %T Clustering Method Based on Hypergraph Morkov Relaxation %0 Journal Article %D 2019 %J Computer Science %R %P 452-456 %V 46 %N 6A %U {https://www.jsjkx.com/CN/abstract/article_18385.shtml} %8 2019-06-14 %X How to embed high dimention spatial-temporal feature into low dimention semantic feature word bag is a typic clustering problem in the Internet of vehicle .Spectral clustering algorithm is recently focused because of its simple computing and global optimal solution,however,the research about the numbers of clusters is relatively little.Tranditional eigengap heuristic method works well if the clusters in the data are very well pronounced.However,the more noisy or overlapping the clusters are,the less effective this heuristic is.This paper proposed a clustering method based on hypergraph markov relaxation (HS-MR method).The basic idea of this algorithm is using the Markov process to formally describe hypergraph and start random walk.In the relaxation process of hypergraph Markov chain,meaningful geometric distribution of data set is found through tth power of random transfer matrix P and diffusion mapping.Then,the objective function based on mutual information is proposed to automatically converge the clustering number.Finally,the experimental results show that the algorithm is superior to simple graph spectral clustering algorithm and hypergraph spectral clustering algorithm in accuracy rate.