Genetic algorithm using iterative shrinking for solving clustering problems

An iterative shrinking algorithm has been recently proposed to replace merge-based agglomerative clustering. In this work, we first extend this idea to the case where the number of clusters must also be solved. We then integrate the method within genetic algorithms that uses iterative shrinking for crossover. The proposed method outperforms all other clustering algorithms we have tested. We therefore conclude that this method is the best existing clustering algorithm in terms of minimizing the distortion function value.