The invention discloses a multi-channel
spectral clustering method based on local density
estimation and
neighbor relation spreading. The multi-channel spectrum clustering method based on local density
estimation and
neighbor relation spreading mainly solves the problem that an existing clustering method cannot carry out clustering on data distributed unevenly in density. The multi-channel spectrum clustering method based on local density
estimation and
neighbor relation spreading comprises the steps that local density of a sample is estimated and is used as data characteristics and dimension lifting is carried out on
original data; a
distance matrix, a threshold value and a
similarity matrix are calculated, and a neighbor relation matrix is initialized; the neighbor relation matrix and the
similarity matrix are updated, and similarity of samples of a subset is updated by the adoption of a local maximum similar value, and an accurate
affinity matrix is obtained; a
similarity matrix and a normalized
Laplacian matrix are calculated; a spectrum matrix is normalized, and a clustering result is obtained through the K-means
algorithm. Compared with an existing clustering technology, the multi-channel spectrum method based on local density estimation and neighbor relation spreading enables a more real similarity matrix to be obtained, the clustering result is more accurate and the robustness is better.