K-harmonic means data clustering with Differential Evolution

K-harmonic means clustering algorithm (KHM) is a center-based like K-means (KM), which uses the harmonic averages of the distances from each data point to the centers as components to its performance function and overcomes KM´s one major drawback that is highly dependent on the initial identification of elements that represent the clusters. However, KHM is also easily trapped in local optima. In this paper, a hybrid data clustering algorithm DEKHM based on Differential Evolution (DE) and KHM is proposed, which makes full use of the merits of both algorithms. The DEHKM algorithm not only helps KHM clustering escape from local optima but also overcomes the shortcoming of the slow convergence speed of the DE algorithm. The experiment results on three popular data sets illustrate the superiority and the robustness of the DEKHM clustering algorithm.