The invention discloses a K-means initial clustering center optimization method on the basis of neighborhood information and the
mean difference degree. The method includes the steps that 1, a sampleset X={X1, X2, ..., Xi, ..., Xn} with n objects is input, the clustering number K is determined, and the current determined initial clustering center number k=0 is initialized; 2, a
distance matrix Dis formed; 3, the neighborhood
radius value
delta is determined; 4, the number Ni of samples in the
delta neighborhood of each sample point Xi is calculated, and a matrix N is formed; 5, the sample point Xi corresponding to the maximum
sample number Ni in the
delta neighborhood in N is regarded as the first clustering center C1, k=k+1, and the corresponding Ni in N is set as 0; 6, the sample pointXj corresponding to the maximum
sample number Nj in the delta neighborhood in N is searched for, the distances between Xj and the clustering centers {C1, C2, ..., Ck} are calculated, and the corresponding Nj in N is set as 0; 7, if the distances between Xj and the clustering centers are not less than the
mean difference degree M, k=k+1, C(k+1)=Xj, or Step 6 is returned to; 8, if the current clustering center number k is equal to the clustering sort number K, K initial clustering centers are output, or Step 6 is returned to; 9, the whole sample set is clustered by means of the K-means clustering
algorithm, and a clustering result is output.