Data Clustering with Differential Evolution Incorporating Macromutations

Data clustering is one of the fundamental tools in data mining and requires the grouping of a dataset into a specified number of nonempty and disjoint subsets. Beside the usual partitional and hierarchical methods, evolutionary algorithms are employed for clustering as well. They are able to find good quality partitions of the dataset and successfully solve some of the shortcomings that the k-means, being one of the most popular partitional algorithms, exhibits. This paper proposes a differential evolution algorithm that includes macromutations as an additional exploration mechanism. The application probability and the intensity of the macromutations are dynamically adjusted during runtime. The proposed algorithm was compared to four variants of differential evolution and one particle swarm optimization algorithm. The experimental analysis conducted on a number of real datasets showed that the proposed algorithm is stable and manages to find high quality solutions.