A New Incremental Pairwise Clustering Algorithm

Pairwise clustering methods are able to handle relational data, in which a set of objects is described via a matrix of pairwise (dis)similarities. Here, we consider a cost function for pairwise clustering which maximizes model entropy under the constraint that the error for reconstructing objects from class information is fixed to a small value. Based on the analysis of structural transitions, we derive a new incremental pairwise clustering method which increases the number of clusters until a certain value of a Lagrange multiplier is reached. In addition, the calculation of phase transitions is used for speed-up. The incremental duplication of clusters helps to avoid local optima, and the stopping criterion automatically determines the number of clusters. The performance of the method is assessed on artificial and real-world data.