earticle

영어

Kernel K-means clustering (KKC) is an effective nonlinear extension of K-means clustering, where all the samples in the initial space are mapped into the feature space and then K-means clustering is performed based on the mapped data. However, all the mapped data are expressed by the implicit form, which causes the initial cluster centers can’t be selected flexibly. Once the selected initial cluster centers aren’t suitable, it tends to fall into local optimal solutions and can’t guarantee stable result. Based on a standard orthogonal basis of the sub-space spanned by all the mapped data, a novel improving non-linear algorithm of KKC is presented in this paper. The novel algorithm can express the mapped data using the explicit form, which make it very flexible to select the initial cluster centers as the linear K-means clustering does. Moreover, the computational complexity of the presented algorithm is also significantly reduced compared to that of KKC. The results of simulation experiments illustrate the proposed method can eliminate the sensitivity to the initial cluster centers and simplify computational processing.