Kernel-based adaptation for semi-supervised clustering
基于核自调整进行半监督聚类

Semi-supervised clustering combines unsupervised clustering algorithms with limited background knowledge. However, setting of kernel parameters involved in SSKK(semi-supervised kernel-based kmeans) is still needed to manually regularize. The chosen value of critical parameters will largely effect on the result. The pair constraints were incorporated into clustering object function, and kernel parameters were optimized iteratively during clustering process till to determine optimal RBF kernel. Furthermore, SSKKOK algorithm was proposed, which was based on optimal kernel computation and SSKK algorithm. The experiment demonstrates the algorithm can not only utilize the effect of SSKK , but also automatically set involved parameters.