Cluster of tweet users based on optimal set

Over the years or even decades, researchers are dealing with the problem of duplicate clusters or overlapping clusters in a cluster set. Clusters overlap within each other just as in the case of social networking groups, or grouping movies by genre. In this paper, hierarchical form of clustering is used to cluster user based on interaction which creates numerous clusters with different sizes at different hierarchical level. In doing so, many overlapping clusters are generated but duplicates are not removed. Duplicity possesses a challenge for differentiation. Our work here is two fold. Firstly, to cluster users with different hierarchical levels to generate sets of clusters by level and secondly, to find among the different cluster sets the optimal one by simply using mean and standard deviation. The sense of optimality is different for different requirements. Our work shows that we can have a choice of picking the optimal set by requirement.