Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 452-456.

• Big Data & Data Mining • Previous Articles     Next Articles

Clustering Method Based on Hypergraph Morkov Relaxation

GUO Peng1,2, LI Ren-fa1, HU Hui3   

  1. College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China1;
    College of Computer and Communication,Hunan Institute of Engineering,Xiangtan,Hunan 411104,China2;
    School of Electrical and Information,Hunan Institue of Engineering,Xiangtan,Hunan 411104,China3
  • Online:2019-06-14 Published:2019-07-02

Abstract: 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.

Key words: Hypergraph laplacian, Markov relaxation, Diffusion map, Mutual informaion

CLC Number: 

  • TP391
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