Hubs　are　the　data　instances　appearing　frequently　on　the　nearest　neighbours　lists.　As　the　hubs　of　a　high-dimensional　dataset　are　close　to　the　centres　of　clusters　or　sub-clusters,　some　of　them　are　selected　as　the　centres　of　clusters　by　hub　based　clustering　algorithms.　In　the　process　of　hub　selection,　these　algorithms　rank　data　instances　in　terms　of　their　global　hubness　scores　computed　upon　their　nearest　neighbours　lists,　ignoring　cluster　related　information　such　as　their　labels,　their　and　their　related　instances＇　clustering　quality.　As　a　result,　some　suitable　hubs　may　be　neglected.　To　solve　this　problem,　we　suggest　evaluating　instances　by　their　relative　hubness　scores.　Moreover,　we　propose　a　weighted　relative　hubness　score　computed　upon　nearest　neighbours　lists　and　silhouette　information.　Besides,　we　suggest　selecting　the　instance　of　the　highest　silhouette　information　when　two　or　more　instances　tie　for　first　place.　Experimental　results　on　real　datasets　and　synthetic　datasets　suggest　that　both　the　relative　hubness　score　and　the　weighted　relative　hubness　score　can　improve　hub　based　clustering,　and　the　weighted　relative　hubness　score　often　plays　better.