Abstract: As an emerging clustering algorithm,the similarity definition of spectral clustering between data points plays an important role in its clustering results.Traditional spectral clustering algorithms typically use gaussian kernel function to be similarity function,but it doesn’t make great effects on multidimensional data.On the basis of defining the new similarity function, a density self-adaption semi-supervised clustering algorithm was put forward which is sensitive with density.Combining with constraint theory in pairs of the semi-supervised clustering,the algorithm makes adaptations on similarity between sample points by using priori information,thus improving the accuracy of data.The algorithm achieves good results both in synthetic datasets and real-world datasets.

Key words: Density,Semi-supervised,Spectral clustering