Document clustering with pairwise constraints

This paper proposes document clustering using Kohonen neural network with pairwise constraints. This algorithm works directly on textual information without mapping document into some representation that has quantitative features. The input level of the proposed neural network can directly receive a qualitative value without mapping the qualitative value into the numerical value. The proposed neural network is based on the architecture of text processing Kohonen neural network, the concepts of dissimilarity measure of symbolic objects and pairwise constrained concepts. As a result, the model can successfully assign cluster label to the objects.