Interactive Bayesian Hierarchical Clustering
This addresses the challenge for users who need customized hierarchical clustering, though it is incremental as it extends existing constraint-based methods to hierarchical clustering.
The paper tackled the problem of aligning hierarchical clustering with user needs by designing an interactive Bayesian algorithm that incorporates user constraints and intelligent querying, showing promising results on real data.
Clustering is a powerful tool in data analysis, but it is often difficult to find a grouping that aligns with a user's needs. To address this, several methods incorporate constraints obtained from users into clustering algorithms, but unfortunately do not apply to hierarchical clustering. We design an interactive Bayesian algorithm that incorporates user interaction into hierarchical clustering while still utilizing the geometry of the data by sampling a constrained posterior distribution over hierarchies. We also suggest several ways to intelligently query a user. The algorithm, along with the querying schemes, shows promising results on real data.