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Title: Exact and Approximate Hierarchical Clustering with A*
Hierarchical clustering is a critical task in numerous domains. Many approaches are based on heuristics and the properties of the resulting clusterings are studied post hoc. However, in several applications, there is a natural cost function that can be used to characterize the quality of the clustering. In those cases, hierarchical clustering can be seen as a combinatorial optimization problem. To that end, we introduce a new approach based on A* search. We overcome the prohibitively large search space by combining A* with a novel \emph{trellis} data structure. This combination results in an exact algorithm that scales beyond previous state of the art, from a search space with 10^12 trees to 10^15 trees, and an approximate algorithm that improves over baselines, even in enormous search spaces that contain more than 10^1000 trees. We empirically demonstrate that our method achieves substantially higher quality results than baselines for a particle physics use case and other clustering benchmarks. We describe how our method provides significantly improved theoretical bounds on the time and space complexity of A* for clustering.
Authors:
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Award ID(s):
1763618
Publication Date:
NSF-PAR ID:
10290571
Journal Name:
The Conference on Uncertainty in Artificial Intelligence (UAI)
Sponsoring Org:
National Science Foundation
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