Hub-Based Initialization for K-hubs

K-hubs is a Hub-based clustering algorithm that is very sensitive to initialization. Therefore, this paper proposes an initialization method based on Hub to solve this problem. The initialization method takes full use of the feature of the Hubness phenomenon by selecting initial centers that are the most remote Hub points with each other. The experimental results show that compared with the random initialization of ordinary K-hubs algorithm, the proposed initialization method can obtain a better distribution of initial centers, which could enhance the clustering accuracy; moreover, the selected initial centers can appear near the cluster centers, which could speed up the convergence of the clustering algorithm.