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Computer Science > Machine Learning

arXiv:1602.03258 (cs)
[Submitted on 10 Feb 2016 (v1), last revised 27 Apr 2016 (this version, v3)]

Title:Interactive Bayesian Hierarchical Clustering

Authors:Sharad Vikram, Sanjoy Dasgupta
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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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:1602.03258 [cs.LG]
  (or arXiv:1602.03258v3 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1602.03258

Submission history

From: Sharad Vikram [view email]
[v1] Wed, 10 Feb 2016 03:59:57 UTC (2,050 KB)
[v2] Fri, 12 Feb 2016 23:39:15 UTC (2,050 KB)
[v3] Wed, 27 Apr 2016 01:36:46 UTC (2,050 KB)
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