Interactive Bayesian Hierarchical Clustering

Abstract

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.

References in corpus (3)

• Bayesian Agglomerative Clustering with Coalescents
• Gibbs fragmentation trees
• Local algorithms for interactive clustering

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• Learning-Augmented $k$-means Clustering
• Bounded Memory Active Learning through Enriched Queries
• Near-Optimal Comparison Based Clustering