Kernel parameter optimization for semi-supervised fuzzy clustering with pairwise constraints

A critical problem related to semi-supervised kernel clustering is the selection of an optimal kernel parameter since the value of parameter has significant impact on the performance of clustering. In this paper, we construct a semi-supervised kernel fuzzy c-means clustering algorithm in terms of pairwise constraints to obtain an optimal kernel parameter. Combined with kernel parameter initialization directly using the given constraints, a new optimization process is derived to automatically estimate the optimal parameter of kernel function. Experimental results show that, with the effective use of pairwise constraints, the proposed approach works well for the estimation of kernel parameter in semi-supervised kernel fuzzy c-means clustering.