Abstract
In this paper, we proposed a novel algorithm for pairwise constrained k-means clustering. One of the major problems in the previous algorithms is that the calculation may be stopped when clusters satisfying the constraints cannot be found. The proposed algorithm can partition objects keeping the pairwise constraints using a permutation matrix and thus avoid the problem in the previous studies. A simulation study is performed for assessing an alternating least-squares algorithm for pairwise constrained k-means clustering. The developed algorithm and its applications are illustrated with the two real data examples.
