Semi-supervised agglomerative hierarchical clustering algorithms with pairwise constraints
Sadaaki Miyamoto, Akihisa Terami · 2010
Recently semi-supervised clustering has been studied by many researchers, but there are no extensive studies using different types of algorithms. In this paper we consider agglomerative hierarchical algorithms with pairwise constraints. The constraints are directly introduced to the single linkage which is equivalent to the transitive closure algorithm, while the centroid method and the Ward methods need kernelization of the algorithms. Simple numerical examples are shown to see how the constraints work.