Genetic programming-based clustering using an information theoretic fitness measure

A clustering method based on multitree genetic programming and an information theoretic fitness is proposed. A probabilistic interpretation is given to the output of trees that does not require a conflict resolution phase. The method can cluster data with irregular shapes, estimate the underlying models of the data for each class and use those models to classify unseen patterns. The proposed scheme is tested on several real and artificial data sets, outperforming k-means algorithm in all of them.