In this paper global similarity features that improve speaker clustering based on standard bottom-up clustering are proposed. The novelty of this approach lies in the fact that it exploits the hypothesis that audio segments belonging to the same speaker cluster should demonstrate similar global behavior, exhibit the same similarity and dissimilarity with all the other segments. Every segment is represented by a global similarity vector whose components are encoded by the distance between that segment and each of the other segments to be clustered. The distance between global similarity vectors is used for pre-selection of segment pairs having high global similarity for further merging. In this paper inter-segment distance for global similarity vectors based on Bayesian Information Criterion (BIC) and based on adapted cross likelihood ratio (CLR) are investigated. The evaluation, performed on radio programs, shows that the proposed approach represents an improvement in comparison with the baseline clustering.