Local density-based similarity matrix construction for spectral clustering

According to local and global consistency characteristics of sample data points' distribution, a spectral clustering algorithm using local density-based similarity matrix construction was proposed. Firstly, by analyzing distribution characteristics of sample data points, the definition of local density was given, sorting operation on sample point set from dense to sparse according to sample points' local density was did, and undirected graph in accordance with the designed connection strategy was constructed; then, on the basis of GN algorithm's thinking, a calculation method of weight matrix using edge betweenness was given, and similarity matrix of spectral clustering via data conversion was got; lastly, the class number by appearing position of the first eigengap maximum was determined, and the classification of sample point set in eigenvector space by means of classical clustering method was realized. By means of artificial simulative data set and UCI data set to carry out the experimental tests, results show that the proposed spectral algorithm has better clustering capability.