  Semi-supervised clustering aims to introduce prior knowledge in the decision
process of a clustering algorithm. In this paper, we propose a novel
semi-supervised clustering algorithm based on the information-maximization
principle. The proposed method is an extension of a previous unsupervised
information-maximization clustering algorithm based on squared-loss mutual
information to effectively incorporate must-links and cannot-links. The
proposed method is computationally efficient because the clustering solution
can be obtained analytically via eigendecomposition. Furthermore, the proposed
method allows systematic optimization of tuning parameters such as the kernel
width, given the degree of belief in the must-links and cannot-links. The
usefulness of the proposed method is demonstrated through experiments.
