In the combined method of HITS and K-means algorithms, we first rank the instances and patterns based on their bipartite graph and then run K-means to cluster instances in our annotated dataset.
instead of choosing the instance nearest to the centroid, we retain the one that has the highest HITS hubness score in each cluster.