  Hierarchical clustering is an important technique to organize big data for
exploratory data analysis. However, existing one-size-fits-all hierarchical
clustering methods often fail to meet the diverse needs of different users. To
address this challenge, we present an interactive steering method to visually
supervise constrained hierarchical clustering by utilizing both public
knowledge (e.g., Wikipedia) and private knowledge from users. The novelty of
our approach includes 1) automatically constructing constraints for
hierarchical clustering using knowledge (knowledge-driven) and intrinsic data
distribution (data-driven), and 2) enabling the interactive steering of
clustering through a visual interface (user-driven). Our method first maps each
data item to the most relevant items in a knowledge base. An initial constraint
tree is then extracted using the ant colony optimization algorithm. The
algorithm balances the tree width and depth and covers the data items with high
confidence. Given the constraint tree, the data items are hierarchically
clustered using evolutionary Bayesian rose tree. To clearly convey the
hierarchical clustering results, an uncertainty-aware tree visualization has
been developed to enable users to quickly locate the most uncertain
sub-hierarchies and interactively improve them. The quantitative evaluation and
case study demonstrate that the proposed approach facilitates the building of
customized clustering trees in an efficient and effective manner.
