To deal with the problem that classical spectral clustering methods can not automatically determine the number of class.A new algorithm called automatic spectral clustering(ASC) based on eigengap and orthogonal eigenvector was presented in this paper.The proposed method first constructed the affinity matrix of data

and gained series of eigenvalues and eigenvectors through spectral decomposition.Second

ordered the eigenvalues and used the first maximum eigengap to determine the number of classes.The data was classified by the class number and the angle between two eigenvectors as similarity.The effectiveness of the proposed algorithm was verified on artificial data

and was compared with

k

-means

FCM and Jordan algorithm on UCI database.The experiment results demonstrate that the proposed method ASC outper

forms other three methods in respect of classification accuracy.